Personalized federated learning is proposed to handle the data heterogeneity problem
amongst clients by learning dedicated tailored local models for each user. However, existing …

Large neural networks can be pruned to a small fraction of their original size, with little loss
in accuracy, by following a time-consuming" train, prune, re-train" approach. Frankle & …

In the context of personalized federated learning (FL), the critical challenge is to balance
local model improvement and global model tuning when the personal and global objectives …

In Federated Learning (FL), the clients learn a single global model (FedAvg) through a
central aggregator. In this setting, the non-IID distribution of the data across clients restricts …

In 6G, the communication cost of large-scale distributed machine learning (DML) will be
much higher than the computing cost, which will become a bottleneck restricting the …

The mainstream workflow of image recognition applications is first training one global model
on the cloud for a wide range of classes and then serving numerous clients, each with …

The high communication cost of sending model updates from the clients to the server is a
significant bottleneck for scalable federated learning (FL). Among existing approaches, state …