8 citations · 18 across the 9 of their papers we have counts for
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Personalized Federated Reinforcement Learning via Model-Agnostic Meta-Learning: Convergence of Exact and Hessian-Free Meta-Policy Gradients
Ali Beikmohammadi, Sarit Khirirat, Sindri Magnússon
We study personalized federated reinforcement learning, in which agents, each acting in its own Markov decision process, collaborate through a server to learn a shared MAML-sty…
Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Analysis
Ali Beikmohammadi, Sarit Khirirat, Peter Richtárik +1
Federated reinforcement learning (FedRL) enables collaborative learning while preserving data privacy by preventing direct data exchange between agents. However, many existing FedR…
On the Convergence of Federated Learning Algorithms without Data Similarity
Ali Beikmohammadi, Sarit Khirirat, Sindri Magnússon
Data similarity assumptions have traditionally been relied upon to understand the convergence behaviors of federated learning methods. Unfortunately, this approach often demands fi…
Parallel Momentum Methods Under Biased Gradient Estimations
Ali Beikmohammadi, Sarit Khirirat, Sindri Magnússon
Parallel stochastic gradient methods are gaining prominence in solving large-scale machine learning problems that involve data distributed across multiple nodes. However, obtaining…