# Trustworthy Federated Learning via Blockchain arXiv: https://arxiv.org/abs/2209.04418 ยท HTML: https://mlsanity.com/paper/2209.04418/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Builds on 33 of its 62 references that are in the map. - First posted on arXiv 13 Aug 2022. ## Builds on (most cited first, 30 of 33) - [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019) - [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018) - [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019) - [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018) - [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019) - [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018) - [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021) - [Communication-Efficient Edge AI: Algorithms and Systems](https://mlsanity.com/paper/2002.09668/) (2020) - [Byzantine-Resilient Secure Federated Learning](https://mlsanity.com/paper/2007.11115/) (2020) - [Trustworthy AI: A Computational Perspective](https://mlsanity.com/paper/2107.06641/) (2021) - [A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus](https://mlsanity.com/paper/2004.00773/) (2020) - [Convergence Time Optimization for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2001.07845/) (2020) - [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020) - [Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges](https://mlsanity.com/paper/2104.01776/) (2021) - [Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation](https://mlsanity.com/paper/2101.06905/) (2021) - [Federated Learning via Intelligent Reflecting Surface](https://mlsanity.com/paper/2011.05051/) (2020) - [Accelerating DNN Training in Wireless Federated Edge Learning Systems](https://mlsanity.com/paper/1905.09712/) (2019) - [When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm](https://mlsanity.com/paper/2009.09338/) (2020) - [Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks](https://mlsanity.com/paper/2011.09902/) (2020) - [Mobile Edge Intelligence and Computing for the Internet of Vehicles](https://mlsanity.com/paper/1906.00400/) (2019) - [Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2004.05843/) (2020) - [PIRATE: A Blockchain-based Secure Framework of Distributed Machine Learning in 5G Networks](https://mlsanity.com/paper/1912.07860/) (2019) - [A Survey of Distributed Consensus Protocols for Blockchain Networks](https://mlsanity.com/paper/1904.04098/) (2019) - [Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning](https://mlsanity.com/paper/2003.04544/) (2020) - [A Systematic Survey of Blockchained Federated Learning](https://mlsanity.com/paper/2110.02182/) (2021) - [Towards Secure Blockchain-enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory](https://mlsanity.com/paper/1809.08387/) (2018) - [Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing](https://mlsanity.com/paper/2203.09670/) (2022) - [6G-enabled Edge AI for Metaverse: Challenges, Methods, and Future Research Directions](https://mlsanity.com/paper/2204.06192/) (2022) - [Delay Minimization for Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/2007.03462/) (2020) - [When Mobile Blockchain Meets Edge Computing](https://mlsanity.com/paper/1711.05938/) (2017) ## Built on by (most cited first, 15 of 15) - [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023) - [A Survey on Decentralized Federated Learning](https://mlsanity.com/paper/2308.04604/) (2023) - [Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond](https://mlsanity.com/paper/2306.00419/) (2023) - [FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer](https://mlsanity.com/paper/2405.02685/) (2024) - [Privacy-Preserving in Blockchain-based Federated Learning Systems](https://mlsanity.com/paper/2401.03552/) (2024) - [BC4LLM: Trusted Artificial Intelligence When Blockchain Meets Large Language Models](https://mlsanity.com/paper/2310.06278/) (2023) - [Blockchained Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2305.04513/) (2023) - [Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control](https://mlsanity.com/paper/2210.01452/) (2022) - [Trustworthy Federated Learning: A Survey](https://mlsanity.com/paper/2305.11537/) (2023) - [A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing](https://mlsanity.com/paper/2303.13727/) (2023) - [A Design Framework for operationalizing Trustworthy Artificial Intelligence in Healthcare: Requirements, Tradeoffs and Challenges for its Clinical Adoption](https://mlsanity.com/paper/2504.19179/) (2025) - [AI Explainability and Governance in Smart Energy Systems: A Review](https://mlsanity.com/paper/2211.00069/) (2022) - [Proof of Reasoning for Privacy Enhanced Federated Blockchain Learning at the Edge](https://mlsanity.com/paper/2601.07134/) (2026) - [FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2508.10042/) (2025) - [Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentials](https://mlsanity.com/paper/2602.02629/) (2026) ## Abstract (arXiv) The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving, Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI to guarantee the privacy and security with reliable decisions. As a nascent branch for trustworthy AI, federated learning (FL) has been regarded as a promising privacy preserving framework for training a global AI model over collaborative devices. However, security challenges still exist in the FL framework, e.g., Byzantine attacks from malicious devices, and model tampering attacks from malicious server, which will degrade or destroy the accuracy of trained global AI model. In this paper, we shall propose a decentralized blockchain based FL (B-FL) architecture by using a secure global aggregation algorithm to resist malicious devices, and deploying practical Byzantine fault tolerance consensus protocol with high effectiveness and low energy consumption among multiple edge servers to prevent model tampering from the malicious server. However, to implement B-FL system at the network edge, multiple rounds of cross-validation in blockchain consensus protocol will induce long training latency. We thus formulate a network optimization problem that jointly considers bandwidth and power allocation for the minimization of long-term average training latency consisting of progressive learning rounds. We further propose to transform the network optimization problem as a Markov decision process and leverage the deep reinforcement learning based algorithm to provide high system performance with low computational complexity. Simulation results demonstrate that B-FL can resist malicious attacks from edge devices and servers, and the training latency of B-FL can be significantly reduced by deep reinforcement learning based algorithm compared with baseline algorithms.