2 papers
cs.LG2025
On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations
Guojun Xiong, Shufan Wang, Daniel Jiang +1
Federated reinforcement learning (FedRL) enables multiple agents to collaboratively learn a policy without sharing their local trajectories collected during agent-environment inter…
cs.LG2025
Initializing Services in Interactive ML Systems for Diverse Users
Avinandan Bose, Mihaela Curmei, Daniel L. Jiang +4
This paper investigates ML systems serving a group of users, with multiple models/services, each aimed at specializing to a sub-group of users. We consider settings where upon depl…