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تسجيل مستخدم جديدBlockchain-based Trustworthy Federated Learning Architecture
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Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organisations or devices) train models locally and formulate a global model based on the local model updates without transferring local data externally. However, federated learning systems struggle to achieve trustworthiness and embody responsible AI principles. In particular, federated learning systems face accountability and fairness challenges due to multi-stakeholder involvement and heterogeneity in client data distribution. To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture. We first design a smart contract-based data-model provenance registry to enable accountability. Additionally, we propose a weighted fair data sampler algorithm to enhance fairness in training data. We evaluate the proposed approach using a COVID-19 X-ray detection use case. The evaluation results show that the approach is feasible to enable accountability and improve fairness. The proposed algorithm can achieve better performance than the default federated learning setting in terms of the models generalisation and accuracy.
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Due to the distributed characteristics of Federated Learning (FL), the vulnerability of global model and coordination of devices are the main obstacle. As a promising solution of decentralization, scalability and security, leveraging blockchain in FL
has attracted much attention in recent years. However, the traditional consensus mechanisms designed for blockchain like Proof of Work (PoW) would cause extreme resource consumption, which reduces the efficiency of FL greatly, especially when the participating devices are wireless and resource-limited. In order to address device asynchrony and anomaly detection in FL while avoiding the extra resource consumption caused by blockchain, this paper introduces a framework for empowering FL using Direct Acyclic Graph (DAG)-based blockchain systematically (DAG-FL). Accordingly, DAG-FL is first introduced from a three-layer architecture in details, and then two algorithms DAG-FL Controlling and DAG-FL Updating are designed running on different nodes to elaborate the operation of DAG-FL consensus mechanism. After that, a Poisson process model is formulated to discuss that how to set deployment parameters to maintain DAG-FL stably in different federated learning tasks. The extensive simulations and experiments show that DAG-FL can achieve better performance in terms of training efficiency and model accuracy compared with the typical existing on-device federated learning systems as the benchmarks.
Federated learning is an emerging machine learning paradigm that enables multiple devices to train models locally and formulate a global model, without sharing the clients local data. A federated learning system can be viewed as a large-scale distrib
uted system, involving different components and stakeholders with diverse requirements and constraints. Hence, developing a federated learning system requires both software system design thinking and machine learning knowledge. Although much effort has been put into federated learning from the machine learning perspectives, our previous systematic literature review on the area shows that there is a distinct lack of considerations for software architecture design for federated learning. In this paper, we propose FLRA, a reference architecture for federated learning systems, which provides a template design for federated learning-based solutions. The proposed FLRA reference architecture is based on an extensive review of existing patterns of federated learning systems found in the literature and existing industrial implementation. The FLRA reference architecture consists of a pool of architectural patterns that could address the frequently recurring design problems in federated learning architectures. The FLRA reference architecture can serve as a design guideline to assist architects and developers with practical solutions for their problems, which can be further customised.
Blockchain-enabled Federated Learning (BFL) enables mobile devices to collaboratively train neural network models required by a Machine Learning Model Owner (MLMO) while keeping data on the mobile devices. Then, the model updates are stored in the bl
ockchain in a decentralized and reliable manner. However, the issue of BFL is that the mobile devices have energy and CPU constraints that may reduce the system lifetime and training efficiency. The other issue is that the training latency may increase due to the blockchain mining process. To address these issues, the MLMO needs to (i) decide how much data and energy that the mobile devices use for the training and (ii) determine the block generation rate to minimize the system latency, energy consumption, and incentive cost while achieving the target accuracy for the model. Under the uncertainty of the BFL environment, it is challenging for the MLMO to determine the optimal decisions. We propose to use the Deep Reinforcement Learning (DRL) to derive the optimal decisions for the MLMO.
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Federated learning (FL) has emerged as a promising master/slave learning paradigm to alleviate systemic privacy risks and communication costs incurred by cloud-centric machine learning methods. However, it is very challenging to resist the single poi
nt of failure of the master aggregator and attacks from malicious participants while guaranteeing model convergence speed and accuracy. Recently, blockchain has been brought into FL systems transforming the paradigm to a decentralized manner thus further improve the system security and learning reliability. Unfortunately, the traditional consensus mechanism and architecture of blockchain systems can hardly handle the large-scale FL task due to the huge resource consumption, limited transaction throughput, and high communication complexity. To address these issues, this paper proposes a two-layer blockchaindriven FL framework, called as ChainsFL, which is composed of multiple subchain networks (subchain layer) and a direct acyclic graph (DAG)-based mainchain (mainchain layer). In ChainsFL, the subchain layer limits the scale of each shard for a small range of information exchange, and the mainchain layer allows each shard to share and validate the learning model in parallel and asynchronously to improve the efficiency of cross-shard validation. Furthermore, the FL procedure is customized to deeply integrate with blockchain technology, and the modified DAG consensus mechanism is proposed to mitigate the distortion caused by abnormal models. In order to provide a proof-ofconcept implementation and evaluation, multiple subchains base on Hyperledger Fabric are deployed as the subchain layer, and the self-developed DAG-based mainchain is deployed as the mainchain layer. The experimental results show that ChainsFL provides acceptable and sometimes better training efficiency and stronger robustness compared with the typical existing FL systems.
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