activity
20222024
most citedTowards Federated Long-Tailed Learning

3 citations · 9 across the 5 of their papers we have counts for

collaborators

5 papers

quant-ph20241 cited

Leapfrogging Sycamore: Harnessing 1432 GPUs for 7 Faster Quantum Random Circuit Sampling

Xian-He Zhao, Han-Sen Zhong, Feng Pan +10

Random quantum circuit sampling serves as a benchmark to demonstrate quantum computational advantage. Recent progress in classical algorithms, especially those based on tensor netw…

cs.LG20242 cited

Spectral Co-Distillation for Personalized Federated Learning

Zihan Chen, Howard H. Yang, Tony Q. S. Quek +1

Personalized federated learning (PFL) has been widely investigated to address the challenge of data heterogeneity, especially when a single generic model is inadequate in satisfyin…

cs.LG20243 cited

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

Zikai Xiao, Zihan Chen, Liyinglan Liu +6

Federated Long-Tailed Learning (Fed-LT), a paradigm wherein data collected from decentralized local clients manifests a globally prevalent long-tailed distribution, has garnered co…

cs.LG2023

Personalizing Federated Learning with Over-the-Air Computations

Zihan Chen, Zeshen Li, Howard H. Yang +1

Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients col…

cs.LG20223 cited

Towards Federated Long-Tailed Learning

Zihan Chen, Songshang Liu, Hualiang Wang +3

Data privacy and class imbalance are the norm rather than the exception in many machine learning tasks. Recent attempts have been launched to, on one side, address the problem of l…