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

04/27/2021
by   Mingrui Cao, et al.
0

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.

READ FULL TEXT

page 1

page 12

research
01/09/2021

Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus

Federated learning (FL) is a promising distributed learning solution tha...
research
04/27/2021

Secure and Efficient Federated Learning Through Layering and Sharding Blockchain

Federated learning (FL) has emerged as a promising master/slave learning...
research
08/15/2022

An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation

Since the traffic conditions change over time, machine learning models t...
research
06/26/2023

Performance Analysis and Evaluation of Post Quantum Secure Blockchained Federated Learning

Post-quantum security is critical in the quantum era. Quantum computers,...
research
03/23/2023

Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks

The analysis of distributed techniques is often focused upon their effic...
research
06/07/2022

FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning

Federated learning (FL) has emerged as an effective approach to address ...

Please sign up or login with your details

Forgot password? Click here to reset