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cs.LG2025
Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity
Aggrey Muhebwa, Khotso Selialia, Fatima Anwar +1
Federated learning on heterogeneous (non-IID) client data experiences slow convergence due to client drift. To address this challenge, we propose Kuramoto-FedAvg, a federated optim…
cs.LG2023★ 5 cited
Mitigating Group Bias in Federated Learning for Heterogeneous Devices
Khotso Selialia, Yasra Chandio, Fatima M. Anwar
Federated Learning is emerging as a privacy-preserving model training approach in distributed edge applications. As such, most edge deployments are heterogeneous in nature i.e., th…