5.1k citations · 5.7k across the 37 of their papers we have counts for
4 papers · 2 filters
FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus
Amrit Singh Bedi, Chen Fan, Alec Koppel +4
In this work, we quantitatively calibrate the performance of global and local models in federated learning through a multi-criterion optimization-based framework, which we cast as…
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…
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…