8 citations · 18 across the 9 of their papers we have counts for
9 papers
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…
User-Assisted Collaborative Distributed Inference for Efficient QoS-Aware Autoscaling
Alfreds Lapkovskis, Ali Beikmohammadi, Sindri Magnússon +1
Growing demand for artificial intelligence (AI) inference services requires scalable infrastructure, yet centralized serving costs rise with demand. We propose a collaborative dist…
Trust-Aware Topology Learning for Dynamic Decentralized Federated Learning under Adversaries
Shubham Vaishnav, Murtaza Rangwala, Ali Beikmohammadi +2
In dynamic mobile decentralized federated learning (DFL), adversaries can poison both model updates and the topology information devices use to choose collaborators. We present DMT…
Active Inference for Adaptive Traffic Signal Control in Noisy Nonstationary IoT Environments
Dénes Toth, George Ambroladze, Edwin Sundberg +2
Urban traffic signal control at IoT-instrumented intersections must remain effective under sensor occlusion, weather attenuation, and nonstationary demand. Conventional controllers…
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…
Automatic Fused Multimodal Deep Learning for Plant Identification
Alfreds Lapkovskis, Natalia Nefedova, Ali Beikmohammadi
Plant classification is vital for ecological conservation and agricultural productivity, enhancing our understanding of plant growth dynamics and aiding species preservation. The a…
Compressed Federated Reinforcement Learning with a Generative Model
Ali Beikmohammadi, Sarit Khirirat, Sindri Magnússon
Reinforcement learning has recently gained unprecedented popularity, yet it still grapples with sample inefficiency. Addressing this challenge, federated reinforcement learning (Fe…
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…