From the 1 of 7 linked papers with an AI index.
7 papers
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 +3
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…
Reinforcement Learning in Switching Non-Stationary Markov Decision Processes: Algorithms and Convergence Analysis
Mohsen Amiri, Sindri Magnússon
The paper introduces a Switching Non-Stationary MDP framework where the environment alternates among a finite set of MDPs via a hidden Markov chain, and proves that standard TD lea…
Dynamic and Distributed Routing in IoT Networks based on Multi-Objective Q-Learning
Shubham Vaishnav, Praveen Kumar Donta, Sindri Magnússon
IoT networks often face conflicting routing goals such as maximizing packet delivery, minimizing delay, and conserving limited battery energy. These priorities can also change dyna…
Adaptive Budgeted Multi-Armed Bandits for IoT with Dynamic Resource Constraints
Shubham Vaishnav, Praveen Kumar Donta, Sindri Magnússon
Internet of Things (IoT) systems increasingly operate in environments where devices must respond in real time while managing fluctuating resource constraints, including energy and…
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…