Abstract:
Current wireless applications such as autonomous driving, UAVs, IOT devices, etc. generate
massive amounts of data that can be utilized to train machine learning models for decision
making. Privacy, security and bandwidth constraints have led to the use of federated
learning (FL) in wireless systems, where the problem of learning a centralized ML model is
accomplished by training on client-specific local datasets in a server-based and decentralised
setting. In this work, we propose the decentralized clustered wireless FL (CWFL), where
a novel FL strategy is employed using clustering based on signal-to-noise ratio and channel
strength of wireless links between the clients. We show that the proposed technique requires
fewer channels as compared to the decentralized FL technique. Using the MNIST dataset,
we demonstrate that CWFL outperforms the vanilla-FL and a power-allocation based improved
FL strategy with respect to average accuracy and robustness across iterations