Federated Learning is a decentralized framework that enables multiple clients to collaboratively train a machine learning model under the orchestration of a central server without sharing their local data. The centrality of this framework represents a point of failure which is addressed in literature by blockchain-based federated learning approaches. While ensuring a fully-decentralized solution with traceability, such approaches still face several challenges about integrity, confidentiality and scalability to be practically deployed. In this paper, we propose Fantastyc, a solution designed to address these challenges that have been never met together in the state of the art.
Federated learning may be subject to both global aggregation attacks and distributed poisoning attacks. Blockchain technology along with incentive and penalty mechanisms have been suggested to counter these. In this paper, we explore verifiable off-chain computations using zero-knowledge proofs as an alternative to incentive and penalty mechanisms in blockchain-based federated learning. In our solution, learning nodes, in addition to their computational duties, act as off-chain provers submitting proofs to attest computational correctness of parameters that can be verified on the blockchain. W…
Blockchain-based federated learning has gained significant interest over the last few years with the increasing concern for data privacy, advances in machine learning, and blockchain innovation. However, gaps in security and scalability hinder the development of real-world applications. In this study, we propose ScaleSFL, which is a scalable blockchain-based sharding solution for federated learning. ScaleSFL supports interoperability by separating the off-chain federated learning component in order to verify model updates instead of controlling the entire federated learning flow. We implemente…
Federated Learning (FL) provides privacy preservation by allowing the model training at edge devices without the need of sending the data from edge to a centralized server. FL has distributed the implementation of ML. Another variant of FL which is well suited for the Internet of Things (IoT) is known as Collaborated Federated Learning (CFL), which does not require an edge device to have a direct link to the model aggregator. Instead, the devices can connect to the central model aggregator via other devices using them as relays. Although, FL and CFL protect the privacy of edge devices but rais…
With the development of communication technologies in 5G networks and the Internet of things (IoT), a massive amount of generated data can improve machine learning (ML) inference through data sharing. However, security and privacy concerns are major obstacles in distributed and wireless networks. In addition, IoT has a limitation on system resources depending on the purpose of services. In addition, a blockchain technology enables secure transactions among participants through consensus algorithms and encryption without a centralized coordinator. In this paper, we first review the federated le…
Message exchange among vehicles plays an important role in ensuring road safety. Emergency message dissemination is usually carried out by broadcasting. However, high vehicle density and mobility usually lead to challenges in message dissemination such as broadcasting storm and low probability of packet reception. This paper proposes a federated learning based blockchain-assisted message dissemination solution. Similar to the incentive-based Proof-of-Work consensus in blockchain, vehicles compete to become a relay node (miner) by processing the proposed Proof-of-Federated-Learning (PoFL) conse…
Polytechnic Institute, Zhejiang University（浙江大学理工学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Public Affairs, Zhejiang University（浙江大学公共管理学院）
Federated learning(FL) is a rapidly growing field and many centralized and decentralized FL frameworks have been proposed. However, it is of great challenge for current FL frameworks to improve communication performance and maintain the security and robustness under malicious node attacks. In this paper, we propose Galaxy Federated Learning Framework(GFL), a decentralized FL framework based on blockchain. GFL introduces the consistent hashing algorithm to improve communication performance and proposes a novel ring decentralized FL algorithm(RDFL) to improve decentralized FL performance and ban…
B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Prediction
Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；University of Delaware（特拉华大学）
Florida International University（佛罗里达国际大学）；Southern Illinois University（南伊利诺伊大学）
Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence along with emerging security and privacy threats. FL enables efficient model generation from local data storage of the edge devices without revealing the sensitive data to any entities. While this paradigm partly mitigates the privacy issues of users' sensitive data, the performance of the FL process can be threatened and reached a bottleneck due to the growing cyber threats and privacy violation techniques. To expedite the proliferation of F…
Device failure detection is one of most essential problems in industrial internet of things (IIoT). However, in conventional IIoT device failure detection, client devices need to upload raw data to the central server for model training, which might lead to disclosure of sensitive business data. Therefore, in this paper, to ensure client data privacy, we propose a blockchain-based federated learning approach for device failure detection in IIoT. First, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT, which enables verifiable integr…
Mobile Edge Computing (MEC) has been a promising paradigm for communicating and edge processing of data on the move. We aim to employ Federated Learning (FL) and prominent features of blockchain into MEC architecture such as connected autonomous vehicles to enable complete decentralization, immutability, and rewarding mechanisms simultaneously. FL is advantageous for mobile devices with constrained connectivity since it requires model updates to be delivered to a central point instead of substantial amounts of data communication. For instance, FL in autonomous, connected vehicles can increase …
Over the recent years, Federated machine learning continues to gain interest and momentum where there is a need to draw insights from data while preserving the data provider's privacy. However, one among other existing challenges in the adoption of federated learning has been the lack of fair, transparent and universally agreed incentivization schemes for rewarding the federated learning contributors. Smart contracts on a blockchain network provide transparent, immutable and independently verifiable proofs by all participants of the network. We leverage this open and transparent nature of smar…
Nowadays, blockchain-based technologies are being developed in various industries to improve data security. In the context of the Industrial Internet of Things (IIoT), a chain-based network is one of the most notable applications of blockchain technology. IIoT devices have become increasingly prevalent in our digital world, especially in support of developing smart factories. Although blockchain is a powerful tool, it is vulnerable to cyber attacks. Detecting anomalies in blockchain-based IIoT networks in smart factories is crucial in protecting networks and systems from unexpected attacks. In…
Federated learning (FL), thanks in part to the emergence of the edge computing paradigm, is expected to enable true real-time applications in production environments. However, its original dependence on a central server for orchestration raises several concerns in terms of security, privacy, and scalability. To solve some of these worries, blockchain technology is expected to bring decentralization, robustness, and enhanced trust to FL. The empowerment of FL through blockchain (also referred to as FLchain), however, has some implications in terms of ledger inconsistencies and age of informatio…
University of Bayreuth（拜罗伊特大学）；FIM Research Center（FIM研究中心）；Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）
Federated machine learning (FL) allows to collectively train models on sensitive data as only the clients' models and not their training data need to be shared. However, despite the attention that research on FL has drawn, the concept still lacks broad adoption in practice. One of the key reasons is the great challenge to implement FL systems that simultaneously achieve fairness, integrity, and privacy preservation for all participating clients. To contribute to solving this issue, our paper suggests a FL system that incorporates blockchain technology, local differential privacy, and zero-know…
Tsinghua University（清华大学）；Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）
The recent advent of various forms of Federated Knowledge Distillation (FD) paves the way for a new generation of robust and communication-efficient Federated Learning (FL), where mere soft-labels are aggregated, rather than whole gradients of Deep Neural Networks (DNN) as done in previous FL schemes. This security-per-design approach in combination with increasingly performant Internet of Things (IoT) and mobile devices opens up a new realm of possibilities to utilize private data from industries as well as from individuals as input for artificial intelligence model training. Yet in previous …
School of Industrial and Systems Engineering Georgia Institute of Technology（佐治亚理工学院工业与系统工程学院）；TieSet Inc.（TieSet公司(或TieSet Inc.)）
A key aspect of Federated Learning (FL) is the requirement of a centralized aggregator to maintain and update the global model. However, in many cases orchestrating a centralized aggregator might be infeasible due to numerous operational constraints. In this paper, we introduce BAFFLE, an aggregator free, blockchain driven, FL environment that is inherently decentralized. BAFFLE leverages Smart Contracts (SC) to coordinate the round delineation, model aggregation and update tasks in FL. BAFFLE boosts computational performance by decomposing the global parameter space into distinct chunks follo…
For the modern world where data is becoming one of the most valuable assets, robust data privacy policies rooted in the fundamental infrastructure of networks and applications are becoming an even bigger necessity to secure sensitive user data. In due course with the ever-evolving nature of newer statistical techniques infringing user privacy, machine learning models with algorithms built with respect for user privacy can offer a dynamically adaptive solution to preserve user privacy against the exponentially increasing multidimensional relationships that datasets create. Using these privacy a…
Royal Military College of Canada（加拿大皇家军事学院）；Universidad Nacional Autónoma de México（墨西哥国立自治大学）
Federated Learning (FL) is a machine learning technique that addresses the privacy challenges in terms of access rights of local datasets by enabling the training of a model across nodes holding their data samples locally. To achieve decentralized federated learning, blockchain-based FL was proposed as a distributed FL architecture. In decentralized FL, the chief is eliminated from the learning process as workers collaborate between each other to train the global model. Decentralized FL applications need to account for the additional delay incurred by blockchain-based FL deployments. Particula…
Federated Learning is a promising machine learning paradigm when multiple parties collaborate to build a high-quality machine learning model. Nonetheless, these parties are only willing to participate when given enough incentives, such as a fair reward based on their contributions. Many studies explored Shapley value based methods to evaluate each party's contribution to the learned model. However, they commonly assume a semi-trusted server to train the model and evaluate the data owners' model contributions, which lacks transparency and may hinder the success of federated learning in practice…
In this paper, we propose FedChain, a novel framework for federated-blockchain systems, to enable effective transferring of tokens between different blockchain networks. Particularly, we first introduce a federated-blockchain system together with a cross-chain transfer protocol to facilitate the secure and decentralized transfer of tokens between chains. We then develop a novel PoS-based consensus mechanism for FedChain, which can satisfy strict security requirements, prevent various blockchain-specific attacks, and achieve a more desirable performance compared to those of other existing conse…
The emerging Federated Edge Learning (FEL) technique has drawn considerable attention, which not only ensures good machine learning performance but also solves "data island" problems caused by data privacy concerns. However, large-scale FEL still faces following crucial challenges: (i) there lacks a secure and communication-efficient model training scheme for FEL; (2) there is no scalable and flexible FEL framework for updating local models and global model sharing (trading) management. To bridge the gaps, we first propose a blockchain-empowered secure FEL system with a hierarchical blockchain…
Resilient Federated Chain: Transforming Blockchain Consensus into an Active Defense Layer for Federated Learning
Department of Computer Science and Artificial Intelligence（计算机科学与人工智能系）；Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)（数据科学与计算智能安达卢西亚研究 institute）；University of Granada（格拉纳达大学）
ConsenSys Health；Imperial College London（帝国理工学院）；UC San Diego（加州大学圣地亚哥分校）；Johns Hopkins Berman Institute of Bioethics（约翰斯·霍普金斯大学伯曼生物伦理研究所）
We propose a novel architecture for federated learning within healthcare consortia. At the heart of the solution is a unique integration of privacy preserving technologies, built upon native enterprise blockchain components available in the Ethereum ecosystem. We show how the specific characteristics and challenges of healthcare consortia informed our design choices, notably the conception of a new Secure Aggregation protocol assembled with a protected hardware component and an encryption toolkit native to Ethereum. Our architecture also brings in a privacy preserving audit trail that logs eve…
University of Electronic Science and Technology of China（电子科技大学）；Macau University of Science and Technology（澳门科技大学）；Southwest Jiaotong University（西南交通大学）；China Telecommunications Corporation（中国电信集团有限公司）
Medical health care centers are envisioned as a promising paradigm to handle the massive volume of data of COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and training the model in a single organization, which is most common weakness due to the privacy and security of raw data communication. To solve this challenging task, we propose a blockchain-based federated learning framework that provides collaborative data training solutions by coordinating multiple hospitals to train and share encrypted federated models withou…
FL-DECO-BC: A Privacy-Preserving, Provably Secure, and Provenance-Preserving Federated Learning Framework with Decentralized Oracles on Blockchain for VANETs
University of Florida（佛罗里达大学）；Virginia Commonwealth University（弗吉尼亚联邦大学）
Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central server. Consequently, secure aggregation protocols for FL have been developed to conceal the local models from the server. However, we show that, by manipulating the client selection process, the server can circumvent the secure aggregation to learn the local models of a victim client, indicating that secure aggregation alone is inadequate for privacy protection. …
FL-DABE-BC: A Privacy-Enhanced, Decentralized Authentication, and Secure Communication for Federated Learning Framework with Decentralized Attribute-Based Encryption and Blockchain for IoT Scenarios
University of Delaware（特拉华大学）；Deakin University（迪肯大学）；University of Oulu（奥卢大学）
Federated learning (FL) is a promising distributed learning solution that only exchanges model parameters without revealing raw data. However, the centralized architecture of FL is vulnerable to the single point of failure. In addition, FL does not examine the legitimacy of local models, so even a small fraction of malicious devices can disrupt global training. To resolve these robustness issues of FL, in this paper, we propose a blockchain-based decentralized FL framework, termed VBFL, by exploiting two mechanisms in a blockchained architecture. First, we introduced a novel decentralized vali…
Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future
Southern Illinois University（南伊利诺伊大学）；University of Asia Pacific（亚太大学）；Green University of Bangladesh（孟加拉国绿色大学）；University of North Texas（北得克萨斯大学）；John Hopkins University（约翰霍普金斯大学）
