RL-Based Federated Learning Framework Over Blockchain (RL-FL-BC)

Ali Riahi*, Amr Mohamed, Aiman Erbad

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Federated learning (FL) paradigms aim to amalgamate diverse data properties stored locally at each user, while preserving data privacy through sharing users' learning experiences and iteratively aggregating their local learning models into a global one. However, the majority of FL architectures with centralized cloud do not guarantee the trust in sharing users' models, and hence, open the door for slowing and/or contaminating the global learning experience. In this paper, we propose a decentralized Blockchain (BC)-based framework and define a comprehensive protocol for exchanging local models, in order to guarantee users' mutual trust while sharing their local learning experiences. We then propose a technique to optimize the global learning experience using Reinforcement Learning (RL), namely RL-FL-BC, to tackle the trade-off between information age of the learning parameters, data skewness (i.e., non-iid), and BC transaction cost (i.e., Ether price). We implement the proposed framework in a realistic containerized environment to facilitate the comparative study of the RL-FL-BC technique with baselines techniques. Our results show the efficacy of the BC-based protocol to facilitate the exchange of both the models' and the optimization parameters to guarantee users' mutual trust, while improving global learning performance compared to baselines techniques.

Original languageEnglish
Pages (from-to)1587-1599
Number of pages13
JournalIEEE Transactions on Network and Service Management
Volume20
Issue number2
DOIs
Publication statusPublished - 1 Jun 2023

Keywords

  • Blockchain (BC)
  • Internet of Things (IoT)
  • deep Q network (DQN)
  • federated learning (FL)
  • machine learning (ML)
  • reinforcement learning (RL)

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