Mitigating Heterogeneities in Federated Edge Learning with Resource- independence Aggregation

Heterogeneities have emerged as a critical challenge in Federated Learning (FL). In this paper, we identify the cause of FL performance degradation due to heterogeneous issues: the local communicated parameters have feature mismatches and feature representation range mismatches, resulting in ineffective global model generalization. To address it, Heterogeneous mitigating FL is proposed to improve the generalization of the global model with resource-independence aggregation. Instead of linking local model contributions to its occupied resources, we look for contributing parameters directly in each node's training results.