TL;DR
FL-HeteroSecure consistently outperforms state-of-the-art baselines, including FedGAN, FMDS-FL, and HFMDS-FL, improving accuracy, accelerating convergence, and reducing communication costs.
Federated learning (FL) enables collaborative model training without sharing raw data, but its robustness is vulnerable to Byzantine clients, especially under non-identically distributed (non-IID) data. In heterogeneous FL, benign client updates may become multimodal and statistically diverse, making it difficult for m...
Federated learning (FL) trains a shared model across data holders that cannot pool their records, but deployments remain bounded by three coupled costs: uplink traffic from repeated model exchange, accuracy loss under statistically heterogeneous clients, and the information that updates still leak. These are usually at...
GRAB-FL is proposed, a graph-aware, Byzantine-resilient FL framework for a bounded gray-box setting in which adversaries may observe global model trajectories and adapt their updates over time but cannot inspect server-side trust states.
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...
The growing volume of data from smart devices offers significant potential for machine learning, yet privacy concerns hinder centralized use. Federated Learning (FL) has emerged as a promising decentralized learning (DL) approach enabling the use of distributed data without compromising privacy. However, practical depl...
FedSentinel is presented, a novel Byzantine-resilient federated learning framework that combines cryptographic gradient attestation with adaptive trust-weighted aggregation to protect against coordinated model-poisoning attacks, which are among the most serious challenges.
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.
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