39 citations · 65 across the 15 of their papers we have counts for
9 papers · 1 filter
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…
First Provable Guarantees for Practical Private FL: Beyond Restrictive Assumptions
Egor Shulgin, Grigory Malinovsky, Sarit Khirirat +1
Federated Learning (FL) enables collaborative training on decentralized data. Differential privacy (DP) is crucial for FL, but current private methods often rely on unrealistic ass…
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…
Smoothed Normalization for Efficient Distributed Private Optimization
Egor Shulgin, Sarit Khirirat, Peter Richtárik
Federated learning enables training machine learning models while preserving the privacy of participants. Surprisingly, there is no differentially private distributed method for sm…
Error Feedback under -Smoothness: Normalization and Momentum
Sarit Khirirat, Abdurakhmon Sadiev, Artem Riabinin +2
We provide the first proof of convergence for normalized error feedback algorithms across a wide range of machine learning problems. Despite their popularity and efficiency in trai…
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…
Clip21: Error Feedback for Gradient Clipping
Sarit Khirirat, Eduard Gorbunov, Samuel Horváth +3
Motivated by the increasing popularity and importance of large-scale training under differential privacy (DP) constraints, we study distributed gradient methods with gradient clipp…
The Convergence of Sparsified Gradient Methods
Dan Alistarh, Torsten Hoefler, Mikael Johansson +3
Distributed training of massive machine learning models, in particular deep neural networks, via Stochastic Gradient Descent (SGD) is becoming commonplace. Several families of comm…