Efficient federated learning
Communication- and parameter-efficient multi-task learning for edge intelligence.
This research explores efficient federated learning through dynamic sparse training, parameter freezing, and task-aware optimization.
Representative work includes PFFDST, which reduces communication cost while improving accuracy, and FedSTA, which mitigates negative transfer in federated multi-task learning.