7 citations · 22 across the 14 of their papers we have counts for
9 papers · 1 filter
Optimized Tradeoffs for Private Prediction with Majority Ensembling
Shuli Jiang, Qiuyi, Zhang +1
We study a classical problem in private prediction, the problem of computing an -differentially private majority of -differentially private algorithms for $1 \…
FedFisher: Leveraging Fisher Information for One-Shot Federated Learning
Divyansh Jhunjhunwala, Shiqiang Wang, Gauri Joshi
Standard federated learning (FL) algorithms typically require multiple rounds of communication between the server and the clients, which has several drawbacks, including requiring…
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models
Yae Jee Cho, Luyang Liu, Zheng Xu +2
Foundation models (FMs) adapt well to specific domains or tasks with fine-tuning, and federated learning (FL) enables the potential for privacy-preserving fine-tuning of the FMs wi…
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices
Jiin Woo, Laixi Shi, Gauri Joshi +1
Offline reinforcement learning (RL), which seeks to learn an optimal policy using offline data, has garnered significant interest due to its potential in critical applications wher…
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems
Neharika Jali, Guannan Qu, Weina Wang +1
We consider the problem of efficiently routing jobs that arrive into a central queue to a system of heterogeneous servers. Unlike homogeneous systems, a threshold policy, that rout…
Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels
Yae Jee Cho, Gauri Joshi, Dimitrios Dimitriadis
Many existing FL methods assume clients with fully-labeled data, while in realistic settings, clients have limited labels due to the expensive and laborious process of labeling. Li…