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pro vyhledávání: '"Yeh, Edmund"'
Addressing intermittent client availability is critical for the real-world deployment of federated learning algorithms. Most prior work either overlooks the potential non-stationarity in the dynamics of client unavailability or requires substantial m
Externí odkaz:
http://arxiv.org/abs/2409.17446
Autor:
Siew, Marie, Zhang, Haoran, Park, Jong-Ik, Liu, Yuezhou, Ruan, Yichen, Su, Lili, Ioannidis, Stratis, Yeh, Edmund, Joe-Wong, Carlee
Federated learning (FL) enables collaborative learning across multiple clients. In most FL work, all clients train a single learning task. However, the recent proliferation of FL applications may increasingly require multiple FL tasks to be trained s
Externí odkaz:
http://arxiv.org/abs/2404.13841
Federated learning is a popular distributed learning approach for training a machine learning model without disclosing raw data. It consists of a parameter server and a possibly large collection of clients (e.g., in cross-device federated learning) t
Externí odkaz:
http://arxiv.org/abs/2404.10091
Emerging edge computing paradigms enable heterogeneous devices to collaborate on complex computation applications. However, for congestible links and computing units, delay-optimal forwarding and offloading for service chain tasks (e.g., DNN with ver
Externí odkaz:
http://arxiv.org/abs/2403.15936
Autor:
Zhang, Jinkun, Yeh, Edmund
Deploying data- and computation-intensive applications such as large-scale AI into heterogeneous dispersed computing networks can significantly enhance application performance by mitigating bottlenecks caused by limited network resources, including b
Externí odkaz:
http://arxiv.org/abs/2403.15927
As edge computing capabilities increase, model learning deployments in diverse edge environments have emerged. In experimental design networks, introduced recently, network routing and rate allocation are designed to aid the transfer of data from sen
Externí odkaz:
http://arxiv.org/abs/2401.04996
Autor:
Mutlu, Faruk Volkan, Yeh, Edmund
Caching is crucial for enabling high-throughput networks for data intensive applications. Traditional caching technology relies on DRAM, as it can transfer data at a high rate. However, DRAM capacity is subject to contention by most system components
Externí odkaz:
http://arxiv.org/abs/2310.07243
Autor:
Zhang, Jinkun, Yeh, Edmund
Collaborative edge computing (CEC) is an emerging paradigm for heterogeneous devices to collaborate on edge computation jobs. For congestible links and computing units, delay-optimal forwarding and offloading for service chain tasks (e.g., DNN with v
Externí odkaz:
http://arxiv.org/abs/2310.06141
Federated learning (FL) is a decentralized learning framework wherein a parameter server (PS) and a collection of clients collaboratively train a model via minimizing a global objective. Communication bandwidth is a scarce resource; in each round, th
Externí odkaz:
http://arxiv.org/abs/2306.00280
Autor:
Zhang, Jinkun, Yeh, Edmund
Caching can be leveraged to significantly improve network performance and mitigate congestion. However, characterizing the optimal tradeoff between routing cost and cache deployment cost remains an open problem. In this paper, for a network with arbi
Externí odkaz:
http://arxiv.org/abs/2303.01648