The new MACBDN approach is highly successful in integrating federated learning, graph representations, and the attention mechanism to maintain an optimal balance between the three parameters, and its lightweight nature and privacy-preserved design help it stay flexible for practical clinical settings and multi-party ho...
Asha Latha Thandu, Pradeepini Gera· The European Physical Journa...· 0 citations
Abstract One of the biggest issues around using machine learning (ML) for cybersecurity in commercial banking is the gap between proof-of-concept and in production. Not every model that has a high accuracy score on a benchmark dataset is the same model as one that is operating in a live transaction pipeline, subject to...
This study is intended to serve as a structured reference for the development of scalable, trustworthy, and intelligent IoV systems by highlighting state-of-the-art techniques, open research gaps, and future directions.
Mohanad Alayedi, Ahmad M. Jaradat· Machine Learning and Knowled...· 0 citations
The growing size and sophistication of the Internet of Things (IoT) has brought up the issue of network security and routing efficiency, especially in unfriendly settings where Denial-of-Service (DoS) attacks are frtaent. Conventional trust-based routing algorithms are not very flexible and are not capable of adequatel...
M. N. Karuppusamy, N. Sasirekha· International Journal of Eng...· 0 citations
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HybridFLow is presented, a closed-loop SDN-driven orchestration framework that integrates network-layer intelligence directly into hybrid FL and generates calibrated per-client communication-time estimates before each training round and uses them to partition clients into synchronous and asynchronous groups while balan...
Osama Abu Hamdan, Rabina Pandey, Hao Che et al.· 0 citations
Federated clients that perform several local optimization steps can return parameter displacements with widely different magnitudes. The quadratic regularization of FedProx grows linearly with displacement and therefore offers limited control over the contrast between ordinary and unusually large client movements. We h...
NEXUS-MI is introduced, a gateway-coordinated federated personalization framework that treats synchronization as an explicit design variable in federated MI personalization and is motivated by joint evaluation of personalized accuracy, communication cost, update freshness, and subject-level reliability.
Simultaneous assessment of medical imaging and patient records is often required in clinical diagnosis. However, standard machine learning algorithms cannot analyze these data types together. Meanwhile, compliance with HIPAA and GDPR can constrain centralized aggregation of sensitive patient data. This leaves a crucial...
Aging clocks quantify biological aging and help characterize individual health status. What protein interactions are important for accurate aging clocks, and are they zeroth-order or higher-order? Addressing these questions requires learning from large molecular datasets distributed across medical centers, where privac...
Chunxu Zhang, Bo Li, Wenliang Wang et al.· 0 citations
Federated learning shares model updates rather than raw data, yet these updates can be inverted to reconstruct the clients'training data. Analytic reconstruction attacks, which invert a gradient in closed form, degrade as the batch grows: prior single-round attacks recover only about half of a batch of size $100$ even...
FedMVLA is proposed, a modality-decoupled FL framework for privacy-preserving embodied intelligence in 6G networks that incorporates three mechanisms: modality-aware federated aggregation (MAFA), modality-aware privacy allocation (MAPA), and modality-aware communication compression (MACO), together with a modality-slic...
This paper considers passive, location-aware, static eavesdroppers without access to legitimate channel state information and proposes secure adaptive federated authentication for multi-zone NTN systems (SAFA-MZ) that maximizes secrecy spectral efficiency (SSE) while ensuring authentication reliability, power limits, a...
Parsa Rajabi, M. Mirzaee, M. Abedi et al.· 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
