A Genetic Algorithm-Based Parameter Selection for Communication-Efficient Federated Learning

Federated Learning (FL) enables decentralized model training without centralized data collection, but high communication overhead remains a key challenge, particularly in bandwidth-constrained environments like IoT and edge networks. Existing FL methods transmit full model updates, which leads to high communication costs. In this paper, we propose Genetic Algorithm-based Selective Parameter Updates (GASPU), a novel approach that uses a Genetic Algorithm (GA) to selectively transmit model parameter updates, significantly reducing communication overhead while maintaining competitive accuracy. GASPU optimizes binary masks allowing only the most effective parameters to be sent. We validate the GASPU approach on the HAR and KWS datasets, which are representative of realistic FL settings. While achieving a 66% reduction in communication overhead over 100 communication rounds on HAR (from 0.49 MB to 0.16 MB) and a reduction to (from 1.27 MB to 0.44 MB) on KWS, GASPU maintained competitive acc...

Federated Learning (FL) enables decentralized model training without centralized data collection, but high communication overhead remains a key challenge, particularly in bandwidth-constrained environments like IoT and edge networks. Existing FL methods transmit full model updates, which leads to high communication costs. In this paper, we propose Genetic Algorithm-based Selective Parameter Updates (GASPU), a novel approach that uses a Genetic Algorithm (GA) to selectively transmit model parameter updates, significantly reducing communication overhead while maintaining competitive accuracy. GASPU optimizes binary masks allowing only the most effective parameters to be sent. We validate the GASPU approach on the HAR and KWS datasets, which are representative of realistic FL settings. While achieving a 66% reduction in communication overhead over 100 communication rounds on HAR (from 0.49 MB to 0.16 MB) and a reduction to (from 1.27 MB to 0.44 MB) on KWS, GASPU maintained competitive accuracy with only 10% drop. Existing methods achieve higher accuracies (above 80%) but at significantly higher communication costs. Further experiments on the MNIST benchmarking dataset confirm GASPU ’s generalizability, achieving only a 0.25% drop in accuracy and a 52% reduction in communication overhead over 10 communication rounds.