FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL Divergence

18 April 2022

Papers citing "FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL Divergence"

9 / 9 papers shown

Title
Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling Feng Zhu Aritra Mitra Robert W. Heath FedML 36 0 0 15 Apr 2025
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis Guangchen Lan Dong-Jun Han Abolfazl Hashemi Vaneet Aggarwal Christopher G. Brinton 124 15 0 09 Apr 2024
Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries Swetha Ganesh Jiayu Chen Gugan Thoppe Vaneet Aggarwal FedML 64 1 0 15 Mar 2024
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity Xu Han Jun Li Wen Chen Zhen Mei Kang Wei Ming Ding H. Vincent Poor 36 2 0 04 Aug 2023
Federated Ensemble-Directed Offline Reinforcement Learning Desik Rengarajan N. Ragothaman D. Kalathil S. Shakkottai OffRL 32 1 0 04 May 2023
Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning Dianbo Liu Vedant Shah Oussama Boussif Cristian Meo Anirudh Goyal Tianmin Shu Michael C. Mozer N. Heess Yoshua Bengio 24 7 0 04 Oct 2022
Federated Offline Reinforcement Learning D. Zhou Yufeng Zhang Aaron Sonabend-W Zhaoran Wang Junwei Lu Tianxi Cai OffRL 31 13 0 11 Jun 2022
CECILIA: Comprehensive Secure Machine Learning Framework Ali Burak Ünal Nícolas Pfeifer Mete Akgun 27 2 0 07 Feb 2022
Federated Learning on Non-IID Data Silos: An Experimental Study Yue Liu Yiqun Diao Quan Chen Bingsheng He FedML OOD 95 946 0 03 Feb 2021