4 papers · 1 filter
Differentially Private Federated Clustering with Random Rebalancing
Xiyuan Yang, Shengyuan Hu, Soyeon Kim +1
Federated clustering aims to group similar clients into clusters and produce one model for each cluster. Such a personalization approach typically improves model performance compar…
Topology-Aware Knowledge Propagation in Decentralized Learning
Mansi Sakarvadia, Nathaniel Hudson, Tian Li +2
Decentralized learning enables collaborative training of models across naturally distributed data without centralized coordination or maintenance of a global model. Instead, device…
Efficient Distributed Optimization under Heavy-Tailed Noise
Su Hyeong Lee, Manzil Zaheer, Tian Li
Distributed optimization has become the default training paradigm in modern machine learning due to the growing scale of models and datasets. To mitigate communication overhead, lo…
Efficient Adaptive Federated Optimization
Su Hyeong Lee, Sidharth Sharma, Manzil Zaheer +1
Adaptive optimization is critical in federated learning, where enabling adaptivity on both the server and client sides has proven essential for achieving optimal performance. Howev…