Federated learning offers a promising paradigm for training machine learning models across distributed creative industry clusters without centralising sensitive cultural or commercial data. However, its deployment in Ugandan creative districts confronts two interrelated statistical challenges: distribution shift across...
The documentation and analysis of crimes against humanity in Uganda confront a dual computational challenge: data are distributed across sensitive, institutionally siloed repositories, and the labels required for supervised learning are exceptionally sparse due to the cost and risk of expert annotation. We introduce a...
Pamela Adong Laker· Zenodo (CERN European Organi...· 0 citations
This work proposes a communication-efficient PFL framework via layer-wise multi-threshold random sketching that supports bidirectional communication using compact low-bit sketches and improves the communication-accuracy tradeoff compared with existing one-bit compression approaches.
Xu Zhang, Xing-Yu Hou, Jia-Cheng Cheng et al.· 0 citations
FedIoC is contributed as a modular framework in which clients fold locally available structured threat indicators into their gradient updates, and is used to pinpoint the non-IID gradient structure as the main driver of recovery and to define the open problem of designing encoders that improve on it.
Manuel Röder, Bibin Babu, F. Schleif· 0 citations
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Retail search systems serve diverse geographic regions with distinct query patterns, vocabularies, and product preferences, creating significant data heterogeneity that challenges both privacy-preserving training and model personalization. Federated learning offers a natural solution for privacy, but standard FL method...
Q. Nguyen, Ali Lafzi, A. Phatak et al.· 0 citations
Experimental results demonstrate that the proposed reinforcement learning-based Multi-agent Federated Deep Q-Network model, which integrates federated learning with QuadTree spatial partitioning and indexing framework for collision detection, is a promising solution for advancing autonomous vehicle routing systems.
A. Raj, Anurag Sharma, K. Naik· Cognitive Computation· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, the inherent vulnerability of FL to malicious participants, known as Byzantine attacks, poses a significant threat to the integrity and convergence of the lear...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized datasets while preserving data privacy. However, current FL security mechanisms often rely on centralized trust assumptions, posing significant vulnerabilities. This paper proposes a novel security framework leverag...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to enhancing data privacy in federated learning by integrating differential privacy with homomorphic encryption. Federated learning, a distributed machine learning paradigm, allows training models across multiple devices without directly sharing raw data. However, this approach stil...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to achieving robust differential privacy within federated learning systems, specifically designed to mitigate the risks posed by Byzantine attacks. Traditional federated learning methods frequently struggle to provide rigorous privacy guarantees when confronted with malicious partic...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to distributed machine learning by enabling collaborative model training without directly exchanging raw data. However, traditional FL systems are plagued by privacy vulnerabilities and significant communication costs. This paper proposes a novel framework, Quantum Wa...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of decentralized machine learning (DML) to autonomous vehicle control. Traditional centralized approaches to autonomous driving often face challenges related to scalability, communication bottlenecks, and vulnerability to single points of failure. This research proposes a novel s...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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.
MIT News · Artificial Intelligence· news.mit.eduOct 7, 2026
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.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
