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1,721 papers
Momentum federated learning (MFL) has attracted widespread attention in privacy-preserving data mining. However, an adversary may engage in two malicious behaviors: (1) launching gradient inversion attacks on momentum information to reconstruct training samples, and (2) tampering with momentum information during aggreg...
A taxonomy of existing methods into supervised and instance-based learning, deep and hybrid architectures, and imbalance-aware learning based on resampling is proposed, and a research-gap table identifies open issues in interpretability, cross-domain transfer, and energy-aware computation.
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RetFL is proposed, a CKKS-enabled robust aggregation framework for DFL that establishes a decentralized training workflow with VRF-based candidate selection and view change, and designs a weighted aggregation scheme that incorporates cosine similarity and a dynamic reputation mechanism to weight updates and suppress pe...
Due to its significant importance in many disciplines, distributed optimization under uncertainty has become an active research field over the past decade. This paper presents a cross-paradigm review of this development. We organize the literature around three approaches ordered by how much is known about the uncertain...
Underwater structural inspection is essential for ship maintenance and marine infrastructure management, but practical inspection data are often distributed across different vessels, ports, inspection platforms and sensing devices. Direct centralized training may be limited by data ownership, communication cost, and op...
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
The effort aims to help U.S. learners from kindergarten to community college, with an emphasis on math, making, and the constructive use of AI.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
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