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1,836 papers
Decentralized Federated Reinforcement Learning (DFRL) presents a promising approach to training robust and adaptable RL agents by leveraging distributed data and computational resources. However, this paradigm is susceptible to attacks from Byzantine actors who can inject malicious updates, jeopardizing the learning pr...
PNFR is designed, a privacy-preserving federated learning scheme for regressions training built on the MPaillier algorithm, which is three times faster than interactive federated regressions PrivFL and about 80% faster than non-interactive federated regressions VANE.
This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpreti...
Amid the global transition toward carbon neutrality, digital technologies such as blockchain and federated learning offer a viable pathway to alleviating green financing constraints for small- and medium-sized enterprises (SMEs) and advancing supply chain decarbonization. Against this backdrop, this paper proposes a di...
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This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpreti...
This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpreti...
The Private Computation Space (PCS), a deployed, open-source Machine Learning system to provision and process farmer data securely and improve the worst single-site model accuracy by 22.4% and 9.1%, respectively, while preserving privacy.
Results indicate that CACTUS can propagate backdoors through repeated DFL aggregation and show that ASR varies with network topology and increases with the malicious-node ratio.
This work proposes SAPE-FL (Similarity-Aware Personalized Federated Learning), a novel personalization framework that anchors each client's model to both the global model and a similarity-weighted peer averaged model that mitigates negative transfer and enhances robustness in heterogeneous settings.
This work forms decentralized prompt tuning as a Wasserstein-based optimization problem over prompt measures, which captures the set-valued structure of prompts and proposes D-FROST, an optimal-transport-based (OT-based) decentralized prompt-tuning algorithm that merges neighborhood prompts into compact representative...
Federated LoRA adaptation improves shared-class AUC on all four cohorts over the unadapted BiomedCLIP backbone over the unadapted BiomedCLIP backbone, showing that the gains come from federated adaptation rather than from the pretrained model's zero-shot ability.
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
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.
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