Blockchained On-Device Federated Learning

Abstract

By leveraging blockchain, this letter proposes a blockchained federated learning (BlockFL) architecture where local learning model updates are exchanged and verified. This enables on-device machine learning without any centralized training data or coordination by utilizing a consensus mechanism in blockchain. Moreover, we analyze an end-to-end latency model of BlockFL and characterize the optimal block generation rate by considering communication, computation, and consensus delays.

to appear in IEEE Communications Letters

References in corpus (3)

• Communication-Efficient Learning of Deep Networks from Decentralized Data
• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications

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