• Corpus ID: 233407918

Secure and Efficient Federated Learning Through Layering and Sharding Blockchain

  title={Secure and Efficient Federated Learning Through Layering and Sharding Blockchain},
  author={Shuo Yuan and Bin Cao and Yaohua Sun and Mugen Peng},
—Introducing blockchain into Federated Learning (FL) to build a trusted edge computing environment for transmission and learning has become a new decentralized learning pattern, which has received extensive attention. However, the traditional consensus mechanism and architecture of blockchain systems can hardly handle the large-scale FL task and run on IoT devices due to the huge resource consumption, limited transaction throughput, and high communication complexity. To address these issues… 

ScaleSFL: A Sharding Solution for Blockchain-Based Federated Learning

This study proposes ScaleSFL, which is a scalable blockchain-based sharding solution for federated learning, and presents a performance evaluation of results collected through Hyperledger Caliper benchmarking tools conducted on model creation, showing that sharding can improve validation performance linearly while remaining efficient and secure.



ChainsFL: Blockchain-driven Federated Learning from Design to Realization

A two-layer blockchain-driven FL framework, called ChainsFL, which is composed of multiple Raft-based shard networks and a Direct Acyclic Graph (DAG)-based main chain (layer-2) where layer-l limits the scale of each shard for a small range of information exchange, and layer-2 allows eachShard to update and share the model in parallel and asynchronously.

Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach

This paper introduces a framework for empowering FL using Direct Acyclic Graph (DAG)-based blockchain systematically (D AG-FL), and shows that DAG-FL can achieve better performance in terms of training efficiency and model accuracy compared with the typical on-device federated learning systems as the benchmarks.

Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles

A new architecture based on federated learning to relieve transmission load and address privacy concerns of providers is proposed and the reliability of shared data is also guaranteed by integrating learned models into blockchain and executing a two-stage verification.

BAFL: A Blockchain-Based Asynchronous Federated Learning Framework

A blockchain-based asynchronous federated learning (BAFL) framework is proposed to ensure the security and efficiency required by FL and has higher efficiency and higher performance for preventing poisoning attacks than other distributed ML methods.

PiRATE: A Blockchain-Based Secure Framework of Distributed Machine Learning in 5G Networks

This article proposes a secure computing framework based on the sharding technique of blockchain, namely PiRATE, which aims to provide the byzantine-resilience for distributed learning in the 5G era.

When Internet of Things Meets Blockchain: Challenges in Distributed Consensus

The basic concept of blockchain is introduced and illustrating why a consensus mechanism plays an indispensable role in a blockchain enabled IoT system, and two mainstream DAG based consensus mechanisms, the Tangle and Hashgraph, are reviewed to show why DAG consensus is more suitable for IoT system than PoW and PoS.

FLchain: Federated Learning via MEC-enabled Blockchain Network

  • Umer MajeedC. Hong
  • Computer Science
    2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS)
  • 2019
Qualitative evaluation shows that FLchain is more robust than traditional FL schemes as it ensures provenance and maintains auditable aspects of FL model in an immutable manner.

Low-Latency Federated Learning and Blockchain for Edge Association in Digital Twin Empowered 6G Networks

This article introduces the digital twin wireless networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane and proposes a blockchain empowered federated learning framework running in the DTWN for collaborative computing.

Blockchain-Enabled Smart Contracts: Architecture, Applications, and Future Trends

The operating mechanism and mainstream platforms of blockchain-enabled smart contracts are introduced, and a research framework for smart contracts based on a novel six-layer architecture is proposed.

Performance analysis and comparison of PoW, PoS and DAG based blockchains