# Secure and Efficient Federated Learning Through Layering and Sharding Blockchain

arXiv: https://arxiv.org/abs/2104.13130 · HTML: https://mlsanity.com/paper/2104.13130/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 73 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [Reliable Federated Learning for Mobile Networks](https://mlsanity.com/paper/1910.06837/) (2019)
- [SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead](https://mlsanity.com/paper/1910.01355/) (2019)
- [Convergence Time Optimization for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2001.07845/) (2020)
- [IPFS - Content Addressed, Versioned, P2P File System](https://mlsanity.com/paper/1407.3561/) (2014)
- [Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD](https://mlsanity.com/paper/1803.01113/) (2018)
- [Learning Private Neural Language Modeling with Attentive Aggregation](https://mlsanity.com/paper/1812.07108/) (2018)
- [Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks](https://mlsanity.com/paper/2011.09902/) (2020)
- [Adaptive Federated Learning and Digital Twin for Industrial Internet of Things](https://mlsanity.com/paper/2010.13058/) (2020)
- [Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithm](https://mlsanity.com/paper/1912.11745/) (2019)
- [PIRATE: A Blockchain-based Secure Framework of Distributed Machine Learning in 5G Networks](https://mlsanity.com/paper/1912.07860/) (2019)
- [Performance Analysis of the Raft Consensus Algorithm for Private Blockchains](https://mlsanity.com/paper/1808.01081/) (2018)
- [When Internet of Things Meets Blockchain: Challenges in Distributed Consensus](https://mlsanity.com/paper/1905.06022/) (2019)

## Built on by (most cited first, 10 of 10)

- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [Blockchained Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2305.04513/) (2023)
- [Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions](https://mlsanity.com/paper/2403.00873/) (2024)
- [Dataset Distillation-based Hybrid Federated Learning on Non-IID Data](https://mlsanity.com/paper/2409.17517/) (2024)
- [UnifyFL: Enabling Decentralized Cross-Silo Federated Learning](https://mlsanity.com/paper/2504.18916/) (2025)
- [RepuNet: A Reputation System for Mitigating Malicious Clients in DFL](https://mlsanity.com/paper/2506.19892/) (2025)
- [ZK-HybridFL: Zero-Knowledge Proof-Enhanced Hybrid Ledger for Federated Learning](https://mlsanity.com/paper/2601.22302/) (2026)
- [Combining Self-attention and Dilation Convolutional for Semantic Segmentation of Coal Maceral Groups](https://mlsanity.com/paper/2506.12712/) (2025)
- [SEDULity: A Proof-of-Learning Framework for Distributed and Secure Blockchains with Efficient Useful Work](https://mlsanity.com/paper/2512.13666/) (2025)
- [Secure Decentralized Federated Learning via Gossip and Virtual Voting](https://mlsanity.com/paper/2607.08651/) (2026)

## Abstract (arXiv)

Introducing blockchain into Federated Learning (FL) to build a trusted edge computing environment for transmission and learning has attracted widespread attention as a new decentralized learning pattern. However, traditional consensus mechanisms and architectures of blockchain systems face significant challenges in handling large-scale FL tasks, especially on Internet of Things (IoT) devices, due to their substantial resource consumption, limited transaction throughput, and complex communication requirements. To address these challenges, this paper proposes ChainFL, a novel two-layer blockchain-driven FL system. It splits the IoT network into multiple shards within the subchain layer, effectively reducing the scale of information exchange, and employs a Direct Acyclic Graph (DAG)-based mainchain as the mainchain layer, enabling parallel and asynchronous cross-shard validation. Furthermore, the FL procedure is customized to integrate deeply with blockchain technology, and a modified DAG consensus mechanism is designed to mitigate distortion caused by abnormal models. To provide a proof-of-concept implementation and evaluation, multiple subchains based on Hyperledger Fabric and a self-developed DAG-based mainchain are deployed. Extensive experiments demonstrate that ChainFL significantly surpasses conventional FL systems, showing up to a 14% improvement in training efficiency and a threefold increase in robustness.