Towards a Secure and Reliable Federated Learning using Blockchain
This addresses security and reliability issues in federated learning for distributed machine learning applications, but it is incremental as it combines existing blockchain and incentive techniques.
The paper tackled challenges of reliability, tractability, and anonymity in federated learning by proposing a blockchain-based framework (SRB-FL) with sharding and an incentive mechanism, resulting in an efficient and scalable solution.
Federated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. This technique ensures privacy, communication efficiency, and resource conservation. Despite these advantages, FL still suffers from several challenges related to reliability (i.e., unreliable participating devices in training), tractability (i.e., a large number of trained models), and anonymity. To address these issues, we propose a secure and trustworthy blockchain framework (SRB-FL) tailored to FL, which uses blockchain features to enable collaborative model training in a fully distributed and trustworthy manner. In particular, we design a secure FL based on the blockchain sharding that ensures data reliability, scalability, and trustworthiness. In addition, we introduce an incentive mechanism to improve the reliability of FL devices using subjective multi-weight logic. The results show that our proposed SRB-FL framework is efficient and scalable, making it a promising and suitable solution for federated learning.