A Unified Framework for Fair and Personalized Decentralized Learning under Communication Constraints
A new algorithm DMFL-SQ is proposed, a decentralized multi-task learning algorithm that couples personalized model training over a communication graph with an agnostic mixture fairness objective, while reducing communication through sparsification, quantization, and event-triggered synchronization.