Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

One main challenge in federated learning is the large communication cost of exchanging
weight updates from clients to the server at each round. While prior work has made great …

Neural network pruning particularly channel pruning is a widely used technique for
compressing deep learning models to enable their deployment on edge devices with limited …

Federated learning is an increasingly popular paradigm that enables a large number of
entities to collaboratively learn better models. In this work, we study minimax group fairness …

Abstract In cross-device Federated Learning (FL) environments, scaling synchronous FL
methods is challenging as stragglers hinder the training process. Moreover, the availability …

Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine …

Federated learning (FL) promotes decentralized training while prioritizing data
confidentiality. However its application on resource-constrained devices is challenging due …

Large Language Models (LLMs) achieve state-of-the-art performance but are challenging to
deploy due to their high computational and storage demands. Pruning can reduce model …

Neural network pruning is an essential technique for reducing the size and complexity of
deep neural networks, enabling large-scale models on devices with limited resources …

Neural network pruning has been a well-established compression technique to enable deep
learning models on resource-constrained devices. The pruned model is usually specialized …