25 citations · 64 across the 20 of their papers we have counts for
4 papers · 1 filter
Self-Aware Personalized Federated Learning
Huili Chen, Jie Ding, Eric Tramel +4
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…
Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise
Dusan Jakovetic, Dragana Bajovic, Anit Kumar Sahu +3
We introduce a general framework for nonlinear stochastic gradient descent (SGD) for the scenarios when gradient noise exhibits heavy tails. The proposed framework subsumes several…
Federated Learning Challenges and Opportunities: An Outlook
Jie Ding, Eric Tramel, Anit Kumar Sahu +3
Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce devel…
Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits
Sunwoo Lee, Anit Kumar Sahu, Chaoyang He +1
Local Stochastic Gradient Descent (SGD) with periodic model averaging (FedAvg) is a foundational algorithm in Federated Learning. The algorithm independently runs SGD on multiple w…