Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Issaid, Chaouki Ben"'
In this paper, we introduce a method for fine-tuning Large Language Models (LLMs), inspired by Multi-Task learning in a federated manner. Our approach leverages the structure of each client's model and enables a learning scheme that considers other c
Externí odkaz:
http://arxiv.org/abs/2410.15524
Second-order federated learning (FL) algorithms offer faster convergence than their first-order counterparts by leveraging curvature information. However, they are hindered by high computational and storage costs, particularly for large-scale models.
Externí odkaz:
http://arxiv.org/abs/2410.07662
Federated Learning (FL) offers a promising approach for collaborative machine learning across distributed devices. However, its adoption is hindered by the complexity of building reliable communication architectures and the need for expertise in both
Externí odkaz:
http://arxiv.org/abs/2408.13010
Autor:
Elbakary, Ahmed, Issaid, Chaouki Ben, Shehab, Mohammad, Seddik, Karim, ElBatt, Tamer, Bennis, Mehdi
Federated learning is a machine learning approach where multiple devices collaboratively learn with the help of a parameter server by sharing only their local updates. While gradient-based optimization techniques are widely adopted in this domain, th
Externí odkaz:
http://arxiv.org/abs/2406.06655
Autor:
Christophorou, Christophoros, Ioannou, Iacovos, Vassiliou, Vasos, Christofi, Loizos, Vardakas, John S, Seder, Erin E, Chiasserini, Carla Fabiana, Iordache, Marius, Issaid, Chaouki Ben, Markopoulos, Ioannis, Franzese, Giulio, Järvet, Tanel, Verikoukis, Christos
In the upcoming 6G era, mobile networks must deal with more challenging applications (e.g., holographic telepresence and immersive communication) and meet far more stringent application requirements stemming along the edge-cloud continuum. These new
Externí odkaz:
http://arxiv.org/abs/2403.05277
The growing number of wireless edge devices has magnified challenges concerning energy, bandwidth, latency, and data heterogeneity. These challenges have become bottlenecks for distributed learning. To address these issues, this paper presents a nove
Externí odkaz:
http://arxiv.org/abs/2312.14638
In this paper, we propose to solve a regularized distributionally robust learning problem in the decentralized setting, taking into account the data distribution shift. By adding a Kullback-Liebler regularization function to the robust min-max optimi
Externí odkaz:
http://arxiv.org/abs/2208.13810
Autor:
Elgabli, Anis, Issaid, Chaouki Ben, Bedi, Amrit S., Rajawat, Ketan, Bennis, Mehdi, Aggarwal, Vaneet
Newton-type methods are popular in federated learning due to their fast convergence. Still, they suffer from two main issues, namely: low communication efficiency and low privacy due to the requirement of sending Hessian information from clients to p
Externí odkaz:
http://arxiv.org/abs/2206.08829
Autor:
Soret, Beatriz, Nguyen, Lam D., Seeger, Jan, Bröring, Arne, Issaid, Chaouki Ben, Samarakoon, Sumudu, Gabli, Anis El, Kulkarni, Vivek, Bennis, Mehdi, Popovski, Petar
Publikováno v:
IEEE Transactions on Green Communications and Networking 2021
An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications, examples of which include highly automated manufacturing cells or autonomously interacting harvesting mach
Externí odkaz:
http://arxiv.org/abs/2110.01686
In this article, we study the problem of robust reconfigurable intelligent surface (RIS)-aided downlink communication over heterogeneous RIS types in the supervised learning setting. By modeling downlink communication over heterogeneous RIS designs a
Externí odkaz:
http://arxiv.org/abs/2108.09026