federated learning

NEXUS-MI: Communication-Aware Federated Personalization for Gateway-Coordinated Motor-Imagery Brain-Computer Interfaces

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.

OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis

OmniMed-FL is presented, a controlled systems study of multimodal federated learning for five-class clinical condition classification (Normal, Pneumonia, COVID-19, Pleural Effusion, Cardiomegaly) and benchmarks eight fusion strategies, three initializations, four missing-text imputation rules, and matched federated bas...

A Trust-Network-Based Federated Learning Framework for Multi-Center Aging Clock Prediction

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...

Cascading Gradient Inversion via LT-Code Inspired Peeling in Federated Learning

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...

Modality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G

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...

Distributed Physical Layer Authentication and Collaborative RSMA in Non-Terrestrial Networks via Graph Reinforcement Learning

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...

AI-Based Predictive Maintenance in Industrial Systems

This paper describes a novel AI-driven predictive maintenance approach in the context of industrial systems that overcomes the limitations of the existing approaches including high dependency on the availability of data, model complexity and generalisation. With the integration of transformer-based structures, hybrid l...

AI-Driven Cyber Risk Assessment and Insurance Models

Rising cyber risks require intelligent, flexible and transparent risk management solutions. In this paper, we introduced an AI enabled framework for cyber risk assessment and insurance modeling for the age of cloud computing, which incorporates transparent AI, real-time threat intelligence, and federated learning to ta...

Sharp Robust Aggregation in Federated and Distributed Learning: Exact Byzantine Robustness Coefficients, Interactive Communication Lower Bounds, and Compressed Delayed Momentum

This research release develops sharp mathematical results for Byzantine-robust aggregation, exact communication complexity, and compressed delayed momentum in federated and distributed optimization. The work studies a central question in robust distributed learning: how accurately can a server aggregate vectors when up...

A Review on Machine Unlearning Algorithms and Privacy Protection

The role of unlearning in privacy protection and its dual role in security is examined, where it serves as a defense against poisoning and backdoors but also becomes an attack surface and verifiable unlearning in federated and decentralized environments remains a central open problem.

DPPF: a decentralized privacy-preserved framework using blockchain and federated learning for IoT driven smart cities

Sharp Robust Aggregation in Federated and Distributed Learning: Exact Byzantine Robustness Coefficients, Interactive Communication Lower Bounds, and Compressed Delayed Momentum

This research release develops sharp mathematical results for Byzantine-robust aggregation, exact communication complexity, and compressed delayed momentum in federated and distributed optimization. The work studies a central question in robust distributed learning: how accurately can a server aggregate vectors when up...