113 citations · 290 across the 22 of their papers we have counts for
23 papers
Accelerated Training on Low-Power Edge Devices
Mohamed Aboelenien Ahmed, Kilian Pfeiffer, Heba Khdr +3
Training on edge devices poses several challenges as these devices are generally resource-constrained, especially in terms of power. State-of-the-art techniques at the device level…
Efficient Zeroth-Order Federated Finetuning of Language Models on Resource-Constrained Devices
Mohamed Aboelenien Ahmed, Kilian Pfeiffer, Ramin Khalili +2
Federated Learning (FL) is a promising paradigm for finetuning Large Language Models (LLMs) across distributed data sources while preserving data privacy. However, finetuning such…
Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices
Kilian Pfeiffer, Mohamed Aboelenien Ahmed, Ramin Khalili +1
In recent years, Large Language Models (LLMs) through Transformer structures have dominated many machine learning tasks, especially text processing. However, these models require m…
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…
DISTINQT: A Distributed Privacy Aware Learning Framework for QoS Prediction for Future Mobile and Wireless Networks
Nikolaos Koursioumpas, Lina Magoula, Ioannis Stavrakakis +3
Beyond 5G and 6G networks are expected to support new and challenging use cases and applications that depend on a certain level of Quality of Service (QoS) to operate smoothly. Pre…
A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks
Nikolaos Koursioumpas, Lina Magoula, Nikolaos Petropouleas +5
Progressing towards a new era of Artificial Intelligence (AI) - enabled wireless networks, concerns regarding the environmental impact of AI have been raised both in industry and a…
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…
A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks
Lina Magoula, Nikolaos Koursioumpas, Alexandros-Ioannis Thanopoulos +4
Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner,…
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
Kilian Pfeiffer, Ramin Khalili, Jörg Henkel
Federated learning (FL) is usually performed on resource-constrained edge devices, e.g., with limited memory for the computation. If the required memory to train a model exceeds th…
FedZero: Leveraging Renewable Excess Energy in Federated Learning
Philipp Wiesner, Ramin Khalili, Dennis Grinwald +3
Federated Learning (FL) is an emerging machine learning technique that enables distributed model training across data silos or edge devices without data sharing. Yet, FL inevitably…
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…
Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction Games
Jing Tan, Ramin Khalili, Holger Karl
We propose a multi-agent distributed reinforcement learning algorithm that balances between potentially conflicting short-term reward and sparse, delayed long-term reward, and lear…
Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions
Jing Tan, Ramin Khalili, Holger Karl +1
We formulate computation offloading as a decentralized decision-making problem with autonomous agents. We design an interaction mechanism that incentivizes agents to align private…
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization
Kilian Pfeiffer, Martin Rapp, Ramin Khalili +1
Devices participating in federated learning (FL) typically have heterogeneous communication, computation, and memory resources. However, in synchronous FL, all devices need to fini…
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…
Distributed Learning on Heterogeneous Resource-Constrained Devices
Martin Rapp, Ramin Khalili, Jörg Henkel
We consider a distributed system, consisting of a heterogeneous set of devices, ranging from low-end to high-end. These devices have different profiles, e.g., different energy budg…
VRLS: A Unified Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications
Taylan Şahin, Ramin Khalili, Mate Boban +1
Vehicle-to-vehicle (V2V) communications have distinct challenges that need to be taken into account when scheduling the radio resources. Although centralized schedulers (e.g., loca…
Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage
Taylan Şahin, Ramin Khalili, Mate Boban +1
Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled either by a centralized scheduler residing in the network (e.g., a base station in case of cellular syste…
MSPlayer: Multi-Source and multi-Path LeverAged YoutubER
Yung-Chih Chen, Don Towsley, Ramin Khalili
Online video streaming through mobile devices has become extremely popular nowadays. YouTube, for example, reported that the percentage of its traffic streaming to mobile devices h…
Urban Mobility Scaling: Lessons from `Little Data'
Galen Wilkerson, Ramin Khalili, Stefan Schmid
Recent mobility scaling research, using new data sources, often relies on aggregated data alone. Hence, these studies face difficulties characterizing the influence of factors such…
Multi-Source Multi-Path HTTP (mHTTP): A Proposal
Juhoon Kim, Ramin Khalili, Anja Feldmann +2
Today, most devices have multiple network interfaces. Coupled with wide-spread replication of popular content at multiple locations, this provides substantial path diversity in the…
Towards a System Theoretic Approach to Wireless Network Capacity in Finite Time and Space
Florin Ciucu, Ramin Khalili, Yuming Jiang +2
In asymptotic regimes, both in time and space (network size), the derivation of network capacity results is grossly simplified by brushing aside queueing behavior in non-Jackson ne…