Zobrazeno 1 - 10
of 134
pro vyhledávání: '"Wei, Ermin"'
In cross-device federated learning (FL) with millions of mobile clients, only a small subset of clients participate in training in every communication round, and Federated Averaging (FedAvg) is the most popular algorithm in practice. Existing analyse
Externí odkaz:
http://arxiv.org/abs/2410.01209
In this paper, we propose a family of label recovery problems on weighted Euclidean random graphs. The vertices of a graph are embedded in $\mathbb{R}^d$ according to a Poisson point process, and are assigned to a discrete community label. Our goal i
Externí odkaz:
http://arxiv.org/abs/2407.11163
Autor:
Yi, Lihui, Wei, Ermin
Nash equilibrium is a common solution concept that captures strategic interaction in electricity market analysis. However, it requires a fundamental but impractical assumption that all market participants are fully rational, implying unlimited comput
Externí odkaz:
http://arxiv.org/abs/2404.19236
Machine unlearning strives to uphold the data owners' right to be forgotten by enabling models to selectively forget specific data. Recent advances suggest precomputing and storing statistics extracted from second-order information and implementing u
Externí odkaz:
http://arxiv.org/abs/2404.01712
Autor:
Sun, Zhenyu, Wei, Ermin
Classical convergence analyses for optimization algorithms rely on the widely-adopted uniform smoothness assumption. However, recent experimental studies have demonstrated that many machine learning problems exhibit non-uniform smoothness, meaning th
Externí odkaz:
http://arxiv.org/abs/2403.15244
By allowing users to erase their data's impact on federated learning models, federated unlearning protects users' right to be forgotten and data privacy. Despite a burgeoning body of research on federated unlearning's technical feasibility, there is
Externí odkaz:
http://arxiv.org/abs/2312.01235
To protect users' right to be forgotten in federated learning, federated unlearning aims at eliminating the impact of leaving users' data on the global learned model. The current research in federated unlearning mainly concentrated on developing effe
Externí odkaz:
http://arxiv.org/abs/2308.12502
We study deregulated power markets with strategic power suppliers. In deregulated markets, each supplier submits its supply function (i.e., the amount of electricity it is willing to produce at various prices) to the independent system operator (ISO)
Externí odkaz:
http://arxiv.org/abs/2308.11420
Autor:
Yi, Lihui, Wei, Ermin
With the rapid growth in the demand for plug-in electric vehicles (EVs), the corresponding charging infrastructures are expanding. These charging stations are located at various places and with different congestion levels. EV drivers face an importan
Externí odkaz:
http://arxiv.org/abs/2308.05982
We study the problem of exact community recovery in the Geometric Stochastic Block Model (GSBM), where each vertex has an unknown community label as well as a known position, generated according to a Poisson point process in $\mathbb{R}^d$. Edges are
Externí odkaz:
http://arxiv.org/abs/2307.11196