Zobrazeno 1 - 10
of 224
pro vyhledávání: '"Brinton, Christopher G"'
Autor:
Chang, Zhan-Lun, Han, Dong-Jun, Parasnis, Rohit, Hosseinalipour, Seyyedali, Brinton, Christopher G.
While most existing federated learning (FL) approaches assume a fixed set of clients in the system, in practice, clients can dynamically leave or join the system depending on their needs or interest in the specific task. This dynamic FL setting intro
Externí odkaz:
http://arxiv.org/abs/2410.05662
While traditional federated learning (FL) typically focuses on a star topology where clients are directly connected to a central server, real-world distributed systems often exhibit hierarchical architectures. Hierarchical FL (HFL) has emerged as a p
Externí odkaz:
http://arxiv.org/abs/2409.18448
Federated learning (FL) encounters scalability challenges when implemented over fog networks that do not follow FL's conventional star topology architecture. Semi-decentralized FL (SD-FL) has proposed a solution for device-to-device (D2D) enabled net
Externí odkaz:
http://arxiv.org/abs/2409.17430
Autor:
Han, Dong-Jun, Fang, Wenzhi, Hosseinalipour, Seyyedali, Chiang, Mung, Brinton, Christopher G.
Devices located in remote regions often lack coverage from well-developed terrestrial communication infrastructure. This not only prevents them from experiencing high quality communication services but also hinders the delivery of machine learning se
Externí odkaz:
http://arxiv.org/abs/2408.09522
Matrix computations are a fundamental building-block of edge computing systems, with a major recent uptick in demand due to their use in AI/ML training and inference procedures. Existing approaches for distributing matrix computations involve allocat
Externí odkaz:
http://arxiv.org/abs/2408.05152
Opportunistic spectrum access has the potential to increase the efficiency of spectrum utilization in cognitive radio networks (CRNs). In CRNs, both spectrum sensing and resource allocation (SSRA) are critical to maximizing system throughput while mi
Externí odkaz:
http://arxiv.org/abs/2404.14319
Recently, deep learning approaches have provided solutions to difficult problems in wireless positioning (WP). Although these WP algorithms have attained excellent and consistent performance against complex channel environments, the computational com
Externí odkaz:
http://arxiv.org/abs/2404.15374
Federated learning (FL) is a popular solution for distributed machine learning (ML). While FL has traditionally been studied for supervised ML tasks, in many applications, it is impractical to assume availability of labeled data across devices. To th
Externí odkaz:
http://arxiv.org/abs/2404.09861
To improve the efficiency of reinforcement learning (RL), we propose a novel asynchronous federated reinforcement learning (FedRL) framework termed AFedPG, which constructs a global model through collaboration among $N$ agents using policy gradient (
Externí odkaz:
http://arxiv.org/abs/2404.08003
One of the main challenges of decentralized machine learning paradigms such as Federated Learning (FL) is the presence of local non-i.i.d. datasets. Device-to-device transfers (D2D) between distributed devices has been shown to be an effective tool f
Externí odkaz:
http://arxiv.org/abs/2402.09629