8 accepted papers
Analog in-memory computing (AIMC) performs computation directly within resistive crossbar arrays, offering an energy-efficient platform to scale large vision and language models. However, non-ideal analog device properties make the training on AIMC devices challenging. In particular, its update asym…
As the economic and environmental costs of training and deploying large vision or language models increase dramatically, analog in-memory computing (AIMC) emerges as a promising energy-efficient solution. However, the training perspective, especially its training dynamic, is underexplored. In AIMC h…
Stochastic approximation (SA) that involves multiple coupled sequences has diverse applications, including but not limited to bilevel optimization, meta learning and reinforcement learning. Unfortunately, the existing multi-timescale analysis of multiple-sequence SA (MSSA) implies a slow convergence…
Given the high economic and environmental costs of using large vision or language models, analog in-memory accelerators present a promising solution for energy-efficient AI. While inference on analog accelerators has been studied recently, the training perspective is underexplored. Recent studies ha…
This paper studies distributed online learning under Byzantine attacks. The performance of an online learning algorithm is characterized by (adversarial) regret, and a sublinear bound is preferred. But we prove that, even with a class of state-of-the-art robust aggregation rules, in an adversarial e…
Federated learning (FL) raises new challenges on security risks, especially when the FL system involves Byzantine clients that send corrupted or adversarial messages to the central server for deteriorating the training paradigm. While there is an extensive research on robust algorithms for horizonta…
This paper considers the policy evaluation problem in reinforcement learning with agents of a decentralized and directed network. The focus is on decentralized temporal-difference (TD) learning with linear function approximation in the presence of unreliable or even malicious agents, termed as Byzan…
This contribution deals with distributed finite-sum optimization for learning over networks in the presence of malicious Byzantine attacks. To cope with such attacks, resilient approaches so far combine stochastic gradient descent (SGD) with different robust aggregation rules. However, the sizeable…