On-Device Federated Learning via Blockchain and its Latency Analysis

By Hyesung Kim, Jihong Park, Medhi Bennis, Seong-Lyun Kim. 2018

117 citations recorded by the source corpus.

Subfields: Federated & Distributed Learning, Privacy & Security

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Federated & Distributed Learning

1. Bingsheng He
2. Qinbin Li
3. Yang Liu
4. Qiang Yang
5. Medhi Bennis
6. Zeyi Wen
7. Jihong Park
8. Tianjian Chen

In Privacy & Security

1. Yang Liu
2. Qiang Yang
3. Bingsheng He
4. Qinbin Li
5. Zeyi Wen
6. Tianjian Chen
7. Medhi Bennis
8. Jihong Park

Citations in the corpus

• Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach (2019, background): “However, the most existing work made an optimistic assumption that all the mobile devices will unconditionally participate in federated learning when invited [8], [9], which is not practical in the real world due to resource costs incurred by model training [10].”
• Federated Machine Learning (2019, background): “In [34], researchers have considered a block-chained federated learning (BlockFL) architecture, where mobile devices’ local learning model updates are exchanged and verified by leveraging blockchain.”
• Wireless Network Intelligence at the Edge (2018, methodology): “The local distributed ledger is synchronized with the other devices’ ledgers via DLT such as a blockchain algorithm [160] or a directed acyclic graph (DAG) based Byzantine fault tolerance (BFT) algorithm [162].”
• Wireless Edge Computing With Latency and Reliability Guarantees (2019, background): “This direction of edgeML has been fueled by FL [67], [68], [91]–[94] under a data split architecture (see Fig.”
• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, background): “[85] leverage the blockchain in lieu of the central server in their system, where the block-”
• A Crowdsourcing Framework for On-Device Federated Learning (2019, background): “Such works can narrate towards numerous incentive mechanism design, such as offered tokens in blockchain network [17].”
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, unknown): “To address this problem, the authors in [168] introduce a blockchain-based FL architecture, called BlockFL, in which the participants’ local learning”
• A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks (2018, methodology): “This issue is partially touched in [223] in the scenario of mobile federated learning, where each node connected to the blockchain trains on the same structure of deep neural network with the local data.”
• Consensus-Before-Talk: Distributed Dynamic Spectrum Access via Distributed Spectrum Ledger Technology (2018, methodology): “Towards implementing DLT, distributed consensus algorithms have been studied under both large-scale [20], [21] and small-scale systems [22]–[24], with the scalability in terms of the number of participating users.”
• Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing (2019, methodology): “[49] propose blockchain federated learning (BlockFL) with the devices model update exchanged and verified by leveraging blockchain.”
• Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence (2019, background): “In [73], the proposed blockchain-based federated learning architecture, BlockFL, takes edge nodes as miners.”
• Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, methodology): “Therefore, in [223], combining Blockchain and FL as BlockFL is proposed to realize 1) locally global model updating at each edge device rather a specific server, ensuring device malfunction cannot affect other local updates when updating the global model; 2) appropriate reward mechanism for stimulat”
• Blockchain for AI: Review and Open Research Challenges (2019, background): “The decentralized learning models can help in achieving highly distributed and autonomous learning systems that support fully coordinated local intelligence across all verticals in modern AI systems [58], [59].”
• A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond (2021, background): “In [118], the support of a central coordinator in vanilla FL is replaced by blockchain.”