FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data

1 code implementation • 17 Jan 2024 • Zikai Xiao, Zihan Chen, Liyinglan Liu, Yang Feng, Jian Wu, Wanlu Liu, Joey Tianyi Zhou, Howard Hao Yang, Zuozhu Liu

Federated Long-Tailed Learning (Fed-LT), a paradigm wherein data collected from decentralized local clients manifests a globally prevalent long-tailed distribution, has garnered considerable attention in recent times.

Personalized Federated Learning Representation Learning

Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer

1 code implementation • NeurIPS 2023 • Zikai Xiao, Zihan Chen, Songshang Liu, Hualiang Wang, Yang Feng, Jin Hao, Joey Tianyi Zhou, Jian Wu, Howard Hao Yang, Zuozhu Liu

Data privacy and long-tailed distribution are the norms rather than the exception in many real-world tasks.

On Safeguarding Privacy and Security in the Framework of Federated Learning

no code implementations • 14 Sep 2019 • Chuan Ma, Jun Li, Ming Ding, Howard Hao Yang, Feng Shu, Tony Q. S. Quek, H. Vincent Poor

Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL).

Networking and Internet Architecture