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 +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…
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…
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…
Benchmarking Dynamic SLO Compliance in Distributed Computing Continuum Systems
Alfreds Lapkovskis, Boris Sedlak, Sindri Magnússon +2
Ensuring Service Level Objectives (SLOs) in large-scale architectures, such as Distributed Computing Continuum Systems (DCCS), is challenging due to their heterogeneous nature and…
Reinforcement Learning in Switching Non-Stationary Markov Decision Processes: Algorithms and Convergence Analysis
Mohsen Amiri, Sindri Magnússon
We introduce the Switching Non-Stationary Markov Decision Process (SNS-MDP) framework, in which the environment transitions among a finite set of MDPs governed by a latent Markov c…