113 citations · 141 across the 5 of their papers we have counts for
5 papers
Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning
Jing Tan, Ramin Khalili, Holger Karl
The Intelligent Transportation System (ITS) environment is known to be dynamic and distributed, where participants (vehicle users, operators, etc.) have multiple, changing and poss…
Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey
Kilian Pfeiffer, Martin Rapp, Ramin Khalili +1
With an increasing number of smart devices like internet of things (IoT) devices deployed in the field, offloadingtraining of neural networks (NNs) to a central server becomes more…
Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Application
Jing Tan, Ramin Khalili, Holger Karl +1
We formulate offloading of computational tasks from a dynamic group of mobile agents (e.g., cars) as decentralized decision making among autonomous agents. We design an interaction…
Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning
Taylan Şahin, Ramin Khalili, Mate Boban +1
Performance of vehicle-to-vehicle (V2V) communications depends highly on the employed scheduling approach. While centralized network schedulers offer high V2V communication reliabi…
DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems
Martin Rapp, Ramin Khalili, Kilian Pfeiffer +1
We study the problem of distributed training of neural networks (NNs) on devices with heterogeneous, limited, and time-varying availability of computational resources. We present a…