Zobrazeno 1 - 10
of 1 035
pro vyhledávání: '"WANG, SHIQIANG"'
Open-weight large language model (LLM) zoos allow users to quickly integrate state-of-the-art models into systems. Despite increasing availability, selecting the most appropriate model for a given task still largely relies on public benchmark leaderb
Externí odkaz:
http://arxiv.org/abs/2411.00889
Vertical federated learning trains models from feature-partitioned datasets across multiple clients, who collaborate without sharing their local data. Standard approaches assume that all feature partitions are available during both training and infer
Externí odkaz:
http://arxiv.org/abs/2410.22564
Secure aggregation is concerned with the task of securely uploading the inputs of multiple users to an aggregation server without letting the server know the inputs beyond their summation. It finds broad applications in distributed machine learning p
Externí odkaz:
http://arxiv.org/abs/2410.14035
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
Erasure-coded computing has been successfully used in cloud systems to reduce tail latency caused by factors such as straggling servers and heterogeneous traffic variations. A majority of cloud computing traffic now consists of inference on neural ne
Externí odkaz:
http://arxiv.org/abs/2409.01420
We consider a novel active learning problem motivated by the need of learning machine learning models for health monitoring in wireless body area network (WBAN). Due to the limited resources at body sensors, collecting each unlabeled sample in WBAN i
Externí odkaz:
http://arxiv.org/abs/2408.02849
Federated learning (FL) has emerged as a widely adopted training paradigm for privacy-preserving machine learning. While the SGD-based FL algorithms have demonstrated considerable success in the past, there is a growing trend towards adopting adaptiv
Externí odkaz:
http://arxiv.org/abs/2407.18365
In federated learning (FL), data heterogeneity is the main reason that existing theoretical analyses are pessimistic about the convergence rate. In particular, for many FL algorithms, the convergence rate grows dramatically when the number of local u
Externí odkaz:
http://arxiv.org/abs/2407.15567
Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communication bandwidth is limited. Recent works on the convergence analysis of FL
Externí odkaz:
http://arxiv.org/abs/2404.13804