Federated Learning-Based IoT Big Data Management Approach: History
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Contributor:
Aristeidis Karras
,
Anastasios Giannaros
,
Leonidas Theodorakopoulos
,
George A. Krimpas
,
Gerasimos Kalogeratos
,
Christos Karras
,
Spyros Sioutas

Federated Learning (FL) is poised to play an essential role in extending the Internet of Things (IoT) and Big Data ecosystems by enabling entities to harness the computational power of private devices, thus safeguarding user data privacy. Despite its benefits, FL is vulnerable to multiple types of assaults, including label-flipping and covert attacks. The label-flipping attack specifically targets the central model by manipulating its decisions for a specific class, which can result in biased or incorrect results.

  • federated learning
  • privacy preserving
  • data poisoning
  • Big Data systems

1. Introduction

Federated Learning (FL) and data poisoning attacks are two crucial areas of research that have garnered significant attention in recent years. This section outlines the current progress and activities in these areas. FL is an innovative architecture designed to protect user privacy in machine learning environments in various areas [1][2][3]. It is commonly misunderstood, but this section provides examples to better understand its workings. For instance, when different companies aim to collaborate on a machine learning model training process, FL ensures that each company’s local data remain internal by using encryption technology to transfer parameters between clients and the central server, thereby leading to the creation of a Global Model while preserving privacy.
Horizontal Federated Learning (HFL) is a subset of federated learning that splits datasets horizontally and removes data for training with the same user features, but different users [4][5]. This increases the number of user samples, but HFL may be vulnerable when the user attributes of two datasets overlap significantly, but the users themselves do not. To reduce private information exposure during the processing and transmission of components, HFL can employ homomorphic encryption systems [6][7][8], differential privacy mechanisms [9][10][11], and safe aggregation frameworks [12][13][14]. Other methods include blockchain-based FL [15][16] and multi-task FL [17][18].
Vertical Federated Learning (VFL) is used when the user attributes of two datasets partially overlap, but the users themselves overlap significantly [19][20][21]. This involves splitting the datasets vertically along a user/feature axis and removing data when users are identical, but user attributes vary. Approaches for VFL include SecureBoost, which suggests that all members input user attributes to train jointly, and a privacy-protecting logistic regression model based on VFL with parallelising objects for analysis and increased accuracy results [22][23][24].
Data poisoning attacks are an important topic of study in adversarial machine learning, which try to undermine machine learning algorithms [25][26]. These attacks have been studied on various machine learning models, including support vector machines [27], autoregressive models [28], collaborative filtering based on matrix factorisation [29], and neural networks for graph data [30][31][32]. In the context of multitask learning, Reference [33] offered a poisoning attack technique that differs from the situation in federated machine learning, where machine learning models are constructed based on datasets spread across various nodes/devices.
Federated machine learning is a rapidly growing field and offers opportunities for collaborative machine learning, but it also raises serious security and privacy concerns. To tackle these challenges, various defence strategies have been explored, including differential privacy, secure and robust aggregation, and outlier detection. Differential privacy has grown into an increasingly standard method for maintaining privacy in federated learning, but it can negatively impact the model’s accuracy by introducing random noise to the data. Using secure and robust aggregation methods, such as Median-based aggregation, Trimmed Mean, Krum, and Bulyan, federated learning systems remain secure and robust. Outlier detection methods, such as rejection of adverse effects and Trim, identify and reject adversarial system interference proactively.

2. Data Security Encryption in Federated Learning: Relevant Literature and Approaches

Federated learning employs a distributed machine learning approach that facilitates training directly on devices, obviating the need to share sensitive data with a central server. Despite its advantages, concerns about data privacy and security persist in federated learning [34][35][36][37]. Various techniques, including homomorphic encryption, have been advanced to address these challenges. Homomorphic encryption is particularly noteworthy as it enables operations on encrypted data without necessitating their decryption, thereby maintaining data confidentiality.
In the study by [34], the researchers combined homomorphic encryption with cryptographic tools, such as masking and local model protection, to counter potential model inversion or reconstruction attacks, common threats in the domain of private financial or business data. Their findings affirmed that their proposed approach meets the data privacy standards. Another research work [35] conceptualised a blockchain-backed federated learning model for the Industrial Internet of Things (IIoT).
A different approach was presented in [36], where a system-level client selection method called Dubhe was introduced. This method allows clients to actively engage in training while ensuring their data privacy, using homomorphic encryption. The experiments revealed that Dubhe’s performance, in terms of classification accuracy, is on par with the optimal greedy method, with minimal encryption and communication costs. Another mention is the study in [37], which offered an overview of challenges in federated learning and evaluated existing solutions, notably featuring homomorphic encryption.
Expanding on recent work in federated learning security, the study conducted by Fan et al. introduced a novel data-sharing scheme to enhance both security and efficiency [38]. Within this framework, three principal entities—the Cloud Service Provider (CSP), Data Provider (DP), and Data Requester (DR)—collaborate. Essentially, the DP uploads private data and potentially a re-encryption key to the CSP, allowing data re-encryption to specific user groups. Subsequently, the DR can request and decrypt this re-encrypted data using its private key. The scheme outlines eight critical algorithms, presenting an approach that holds significant promise for improving data-sharing protocols in FL ecosystems.
Following the exploration into federated learning security, another notable study detailed a sophisticated encryption algorithm tailored for plaintext images [39]. This process commences by segmenting the image into R, G, and B channels, with subsequent encryption operations applied uniformly across these channels. Key steps in this approach include the separation of channels, leveraging the 2D-LCLM complex chaotic system to generate pseudo-random sequences, and employing the Zhou Yi Eight Trigrams encryption rule to finalise the encryption. This method underscores the evolution of encryption techniques suitable for multimedia data in modern research landscapes.
In the context of images, as previously noted, another study delved into the scope of blockchain security by highlighting a traceability model for the DAG blockchain system [40]. As images demand robust encryption methods, blockchains seek dependable verification systems. This model applies a witness mechanism, encompassing everything from unit addition to the blockchain to information retrieval for users. In this context, a “unit” serves as the primary data container, encapsulating vital information such as hash values and the public key of the uploader. A defining feature of this model is its steadfast commitment to data integrity. Once a unit is validated, modifying its content becomes challenging since all interconnected units would require alterations, ensuring that unauthorised changes are nearly impossible.
A study examined adaptable encryption and decryption frameworks for CKKS homomorphic encryption, enabling computation on encrypted data without decryption [41]. The CKKS scheme, reliant on the RLWE problem and approximate arithmetic, encodes real numbers into polynomials for encryption with a public key. After remote computation on encrypted data, the client decrypts the received results with its secret key. The paper highlights the importance of secure key generation and evaluation key creation for homomorphic operations. The proposed architectures aim to enhance the efficiency of CKKS encryption without delving into extensive data security details [41]. In conjunction with adaptable encryption for CKKS, the study expects future research to integrate homomorphic encryption with machine learning, potentially exploring HE-integrated federated learning for enhanced data privacy.
Ultimately, reviews of the literature on data security in federated learning emphasise strategies such as homomorphic encryption and blockchain to protect data in machine learning. Various methods are proposed to safeguard sensitive information, from cryptographic techniques to innovative data-sharing schemes, bolstering security while maintaining efficiency. The aim is to forge robust federated learning systems that guarantee data privacy, with emerging research exploring the integration of these security methods with homomorphic encryption for enhanced machine learning applications.

3. Privacy Threats in Federated Learning

Privacy challenges that are inherent within Federated Learning (FL) architectures, particularly pertaining to the extensive interaction across various participating entities, pose significant research concerns [42]. One such crucial threat is the Deep Gradients Leakage attack, which is strategically leveraged by an adversary who acts as an aggregator. Within this attack schema, the adversary capitalises on exploiting the gradients of the model with the underlying intent to extrapolate or infer the private data of the individual participants [43]. This form of attack underscores an intricate manipulation of data and, quite conspicuously, has a direct impact on the inherent privacy of data that are circulated within the FL architecture. Furthermore, the assimilation of Generative Adversarial Network (GAN) attacks within the adversarial framework often sees the attacker using GANs to meticulously generate data, which mirrors the private data of participants [43]. The data generation here is formulated such that it concurrently is metaphorically camouflaged, disguising it as legitimate data and, thus, introducing potential jeopardy to the integrity and confidentiality of the original data.
In conjunction with the previous attacks, the looming threat of poisoning attacks and inference attacks magnifies the privacy dilemma in FL. The former envisages a scenario wherein an adversarial participant malevolently injects data into the training process with the aim to subtly, yet systematically, manipulate the Global Model, consequently propagating erroneous inferences [44]. This perturbation of the learning process is not just detrimental to the model accuracy, but also corrodes the authenticity of predictions, potentially cascading to flawed decision-making processes. On the other hand, inference attacks extrapolate this issue, where an adversary infers the private data of other participants through a strategic exploration of the Global Model [44]. Moreover, range inference attacks refine this adversarial strategy by attempting to ascertain the range of the private data of participants, providing a discreet, yet robust mechanism to violate privacy norms [45]. Thus, these types of attacks lead to a wide and deep invasion of privacy, calling for new and effective strategies to mitigate them.
Addressing privacy threats in Federated Learning (FL) invokes a multilayered approach where technologies like blockchain and Trusted Execution Environments (TEEs) have been significant, yet not entirely impervious to certain attack vectors. The incorporation of blockchain technology serves as a decentralised ledger, which aids in securing transparent record-keeping and transactions, thereby mitigating single-point failures and ensuring a level of accountability within the FL paradigm [43]. Concurrently, TEEs ensure secure computational zones, safeguarding data and models during the computation and offering a protective shield against a spectrum of threats.
However, it is essential to acknowledge that current FL protocols exhibit certain deficiencies in rendering an all-encompassing security framework, thus spotlighting a crucial necessity for more-detailed and in-depth research in this arena [46]. The pressing requirement pivots around designing a security architecture that seamlessly blends robustness and comprehensiveness while also manifesting adaptability to the ever-evolving threat landscape. This ensures sustained, privacy-preserving FL operations among the intricate and dynamic cyber–physical interactions in large-scale IoT systems [47]. This establishes a prolific domain for continuing and prospective research, with a dedicated focus on embedding a sophisticated and well-articulated balance of security and privacy within the FL paradigm. Ensuring that innovative solutions are not merely confined to theoretical frameworks, but extend to practical viability in real-world deployments also stands as a pivotal aspect. Such a pursuit not only enriches the academic discourse around privacy-preserving mechanisms in FL, but also contributes substantively to the operational robustness of FL in large-scale systems where data privacy and security are paramount.

4. Privacy and Security in Federated Learning Systems: State-of-the-Art Defensive Mechanisms

Federated Learning (FL) emerges as a pivotal methodology, enabling Machine Learning (ML) application directly on devices while safeguarding sensitive and private information from unwarranted disclosure and tracking. Despite its innovative approach, FL’s security framework invites further scrutiny, especially within sectors managing exceptionally sensitive data, such as the healthcare industry [48][49][50]. Vulnerabilities in FL, including susceptibility to model poisoning, data heterogeneity, and model inversion attacks, possess the potential to undermine the efficacy of the Global Model [48][49][50][51][52].
Various defensive tactics have been introduced to counter these threats, such as the implementation of robust aggregation algorithms, deploying Multi-Party-Computation (MPC)-based secure aggregation, and the utilisation of trained autoencoder-based anomaly-detection models [49][50][51]. Notably, several state-of-the-art defences against model poisoning attacks, including FedCC, Krum, and Trimmed Mean, have been articulated in the existing literature [51][53].
Nevertheless, these strategies often provide solutions that are parallel and non-intersecting with respect to individual attacks or concerns. Moreover, the meticulous orchestration of collusion among malicious participants can subtly reduce the bias triggered in the poisoned local model—minimising disparities from the poison-free model. This subtlety becomes critical in facilitating stealthy backdoor attacks and eluding a myriad of top-tier defence strategies currently available in FL [52]. Thus, a void exists, signalling an exigent need for additional research aimed at devising potent and encompassing defensive mechanisms to bolster the security infrastructure of FL systems.
Romoa stands out in the arena of Federated Learning (FL) as it applies a logarithm-based normalisation strategy, steering clear of the pitfalls associated with scaled gradients that originate from nefarious entities. This strategic model aggregation method acts as a bulwark against model poisoning attacks, a pertinent concern in the FL framework, where several decentralised nodes collaborate in model training. The stabilisation and integrity of the model during its training phase are crucial to ensure the derived insights and applications remain valid and reliable. Hence, Romoa not only addresses the immediate concerns related to malicious activities in FL, but also underscores the necessity of innovatively confronting challenges to uphold the robustness of decentralised learning models.
Concurrently, Robust Federated Aggregation (RFA) demonstrates a divergent, yet equally significant methodology, emphasising the utilisation of a weighted average of gradients to fortify FL systems against Byzantine attacks. The pertinence of resisting such attacks is elevated in sensitive domains, such as healthcare, where the accuracy and reliability of models can directly impact decision-making and outcomes. RFA, through its adept handling of gradients and ensuring the integrity of the aggregation process, helps to sustain the credibility of FL in environments where malicious actors might seek to destabilise the model. Thus, RFA emerges not merely as a defensive mechanism, but as a vital cog ensuring the seamless operation of FL systems, especially where the veracity of aggregated models is critical.
While Romoa and RFA significantly advance the security mechanisms within FL systems, the journey towards a thoroughly secure, decentralised learning environment remains ongoing and necessitates continual research and development. This becomes particularly poignant in the realms of the IoT and Big Data, where large volumes of data are processed and analysed across various nodes. The challenge extends beyond merely defending against known threats to preemptively identifying and mitigating potential future vulnerabilities within the FL paradigm. The continual evolvement of defence mechanisms, in tandem with the evolution of threats, underscores the dynamic and complex nature of securing FL systems. Therefore, it is imperative for the research community to remain engaged in a persistent exploration of innovative strategies and mechanisms to safeguard FL against a spectrum of threats, ensuring its viability and trustworthiness in diverse applications across varied domains.
Navigating through the landscape of defensive mechanisms in federated learning, a variety of strategies have been spotlighted, each exemplifying unique approaches and methodologies towards mitigating adversarial endeavours. The summary presented in Table 1 encapsulates a selection of these mechanisms, illustrating the diversity and specificity with which each strategy is forged and employed. Notably, while strategies like FedCC and Krum emphasise robust aggregation algorithms and server-side defences, respectively, others like FL-WBC introduce client-based strategies to shield the federated learning model from adversarial attacks. This table not only serves as a confluence of varied defensive strategies, but also underscores the multifaceted nature of the challenges that federated learning systems encounter in maintaining model integrity and privacy preservation. Thus, the mechanisms detailed in Table 1 establish a foundation for a more-detailed and -subtle examination of the architectural and functional aspects of these defences, thereby facilitating subsequent research and progression in the spectrum of secure and robust federated learning. This overview of different mechanisms seeks to build a basic understanding, which will help direct future research and development paths in the field.
Table 1. Summary of defensive mechanisms in federated learning systems.
Mechanism Description Reference
FedCC Employs a robust aggregation algorithm, mitigating both targeted and untargeted model poisoning or backdoor attacks, even in non-IID data scenarios. [51]
Krum Acts as a server-side defence via an aggregation mechanism, but may be susceptible to Covert Model Poisoning (CMP). [53]
Trimmed Mean Server-side aggregation similar to Krum, yet also potentially prone to CMP, aiming to resist model poisoning attacks. [53]
MPC-Based Aggregation Mitigates model inversion attacks by employing a trained autoencoder-based anomaly-detection model during aggregation. [50]
FL-WBC A client-based strategy that minimises attack impacts on the Global Model by perturbing the parameter space during local training. [54]
Romoa Utilises a logarithm-based normalisation to manage scaled gradients from malicious vehicles, resisting model poisoning attacks. [55]
RFA Utilises a weighted average of gradients to resist Byzantine attacks, aiming to establish a robust aggregation method. [56]

4.1. Limitations of Current Defensive Mechanisms in Federated Learning

The development and implementation of defensive mechanisms in Federated Learning (FL) have been imperative for ensuring secure and robust model training in decentralised learning environments. Nonetheless, prevailing methods manifest substantial limitations, hampering their optimal functionality and efficacy in practical scenarios.

Computational and Communication Overhead

A significant limitation is the computational burden imposed on Edge Devices (EDs), which often possess restricted computational resources. This limitation arises from the heavy computation overhead, which is further exacerbated when these devices are tasked with conducting complex learning or defensive processes. Concurrently, the communication overhead is another crucial aspect, predominantly related to the uploading of converged local models’ parameters to a centralised server, where aggregation is performed. This not only demands substantial communication resources, but also exposes the system to potential communication-related vulnerabilities [57].

Knowledge Preservation and Incremental Learning

Moreover, a crucial challenge is associated with the guarantee of preserved knowledge, especially in the context of incremental learning over new local datasets. The current defensive mechanisms may jeopardise the acquired knowledge due to the absence of a solid framework that ensures the stability and plasticity of the learned models during subsequent learning phases. This issue is particularly prominent when FL systems encounter novel data, and adaptive learning becomes crucial to preserving and updating the Global Model appropriately [58].

Security and Privacy Concerns

Security concerns, including susceptibility to various attacks (such as model poisoning, privacy inference, and Byzantine attacks), underscore another essential limitation. While privacy preservation is a cornerstone of FL, ensuring robust defence against intricate attack strategies, particularly those exploiting the decentralised nature of FL, remains a pressing concern. The vulnerabilities related to privacy inference attacks, which aim to infer sensitive information from the shared model updates, and Byzantine attacks, where malicious actors disseminate falsified updates, are notably challenging to mitigate comprehensively [58]. This notwithstanding, novel approaches, namely Romoa and RFA, have been proposed to address some of these challenges by introducing advanced aggregation methods designed to resist various attacks while ensuring robust model training [57].

4.2. Problem Statement

Navigating the management of voluminous data derived from the Internet of Things (IoT) environment, coupled with ensuring privacy within expansive systems through Federated Learning (FL), highlights a complex and multi-dimensional challenge. Specifically, the discourse pivots on the following predominant axes of difficulties:
  • Massive and rapid IoT data: IoT environments are characterised by the generation of immense volumes of data (𝐷𝑖) at an astounding velocity, making effective and efficient data management imperative to prevent systemic bottlenecks and to ensure sustained operational performance.
  • Preserving privacy with FL: Ensuring the data (𝐷𝑖 and 𝐷𝑡) remain localised and uncompromised during Global Model training in FL demands robust methodologies to prevent leakage or unintended disclosure of sensitive information through model updates (𝐖).
  • Label-flipping attacks: These attacks, wherein labels of data instances are maliciously altered (𝐷˜𝑖

and 𝐷˜𝑠), present a pronounced threat to model integrity and efficacy in FL. Here, the design and implementation of defensive mechanisms that can detect, mitigate, or recover from such attacks is of paramount importance.

ℋ(𝐷˜𝑖,𝐷˜𝑠;𝐖)→min𝐖𝐿(𝐷𝑖,𝐷𝑡,𝐖)

where ℋ is the high-level function that seeks to minimise the loss function L given the perturbed data and weight matrix, ensuring the learned model 𝐖

  • is resilient against the attack.
  • Ensuring scalability: Addressing the scale, by ensuring the developed FL model not only counters privacy and security threats, but also scales efficiently to manage and process expansive and diverse IoT Big Data.
  • Technological integration and novelty: While FATE offers a promising federated learning framework and Apache Spark offers fast in-memory data processing, exploring and integrating these technologies in an innovative manner that cohesively addresses IoT Big Data management challenges within an FL paradigm becomes crucial.
    ℋ(𝐷𝑖,𝐷𝑡;𝐖,Ω)→min𝐖,Ω𝐿(𝐷𝑖,𝐷𝑡,𝐖,Ω)

where ℋ is aimed at minimising the loss function L concerning the data, the weight matrix 𝐖, and the model relationship matrix Ω, ensuring a harmonised functionality between the integrated technologies and, also, enabling scalable and efficient data processing and model training across federated nodes.

The core objective of FLIBD is to diligently construct a framework that mitigates the identified challenges. This venture not only seeks to resolve current issues, but also aspires to craft a model that leads, moulds, and enhances forthcoming technological and methodological progress within the sphere of privacy-preserving data management in expansive IoT deployments, leveraging federated learning.

5. Proposed Architecture

FLIBD formulates an insightful architecture, intending to skilfully manage voluminous IoT-originated data whilst concurrently ensuring meticulous privacy preservation across large-scale systems, achieved by integratively employing Federated Learning (FL), Apache Spark, and FATE. The fundamental layers and their concomitant functionalities are delineated below and represented in Figure 1:
Electronics 12 04633 g001
Figure 1. The basic architecture.
  • IoT data harvesting and initial processing layer:
    • Dynamic data acquisition: implements a strategic approach to dynamically harvest, categorise, and preliminarily process extensive IoT data, utilising Apache Spark’s proficient in-memory computation to adeptly handle both streaming and batch data.
  • In situ model training and data privacy layer:
    • Intrinsic local model training: employs FATE to facilitate localised model training, reinforcing data privacy by ensuring data are processed in situ.
    • Data and model security mechanism: integrate cryptographic and obfuscation techniques to safeguard data and model during communication, thus fortifying privacy and integrity.
  • Federated learning and secure model consolidation layer:
    • Privacy-aware federated learning: engages FATE to promote decentralised learning, which encourages local model training and securely amalgamates model updates without necessitating direct data exchange.
    • Model aggregation and resilience: establishes a secure aggregation node that amalgamates model updates and validates them against potential adversarial actions and possible model poisoning.
  • Global Model enhancement and feedback integration layer:
    • Deploy, Enhance, and Evaluate: apply the Global Model to enhance local models, instigating a comprehensive evaluation and feedback mechanism that informs subsequent training cycles.
  • Adaptive scalability and dynamic management layer:
    • Dynamic scalability management: utilises Apache Spark to ensure adaptive scalability, which accommodates the continuous data and computational demands intrinsic to vast IoT setups.
    • Proactive system management: implements AI-driven predictive management and maintenance mechanisms, aiming to anticipate potential system needs and iteratively optimise for both performance and reliability.
In essence, the FLIBD architecture aspires to function as a robust foundation for upcoming advancements in privacy-preserving data management within the continuously evolving IoT environment. It makes an effort to navigate present challenges with viable solutions, whilst concurrently establishing a robust framework beneficial to encouraging future research and development in privacy-preserving methodologies for managing IoT Big Data across large-scale scenarios.

This entry is adapted from the peer-reviewed paper 10.3390/electronics12224633

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