Distribution-Aware Personalised Aggregation for Federated Deep Learning Under Non-IID Data
Federated learning enables collaborative deep neural network training across distributed clients without sharing raw data, yet its performance degrades substantially when local data distributions are non-identically distributed (non-IID). Existing aggregation strategies either treat all clients uniformly or require exp...