Skip to content
Log in
Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

Category

federated learning

1,892 papers

#federated learning Nov 2026

Toward Fair Federated Edge Learning Through Prototype-Guided Distributed Adversarial Networks

Federated learning (FL) is a privacy-preserving machine learning (ML) paradigm that can learn models from distributed datasets owned by mobile terminals (MTs). However, ML models usually contain bias on some user groups/sensitive attributes (e.g., gender and race), which poses additional challenges on FL in real-world...

Kang Wei, Xin-Nan Yuan, Zi-Cong Hong et al. · 0 citations
#federated learning Nov 2026

Traffic-Aware Mobility Management Based on Decentralized Federated Deep Reinforcement Learning in Satellite-Terrestrial Vehicular Networks

Excessive traffic generated by in-vehicle applications can cause congestion or overflow in the transmit buffer queue of connected autonomous vehicles (CAVs), leading to high queuing delays and even service outage in satellite-terrestrial vehicular networks (STVN). Therefore, a decentralized federated deep reinforcement...

Peng-Fei Sun, Yang Liu, Ying-Hui Zhang et al. · 0 citations
#federated learning Nov 2026

Subspace-Guided Unlearning and Recovery: Poisoning Defense for Hierarchical Federated Learning in LEO Satellite Networks

Low Earth Orbit (LEO) satellite networks are increasingly adopting Federated Learning (FL) for privacy-preserving collaborative model training. However, this paradigm remains vulnerable to backdoor attacks where compromised satellites inject hidden triggers into their local model updates. Existing defenses are not suit...

Kang-Cheng Yang, Zhi-Shu Shen, Cheng Tan et al. · 0 citations
#federated learning Nov 2026

Accelerating Federated Learning Under Client Dropout via Joint Bandwidth Allocation and Prototype Fine-Tuning in Mobile Edge Computing Networks

Deploying federated learning (FL) in mobile edge computing (MEC) networks enables collaborative model training while preserving the privacy of raw data. However, due to system heterogeneity, statistical heterogeneity of mobile clients (MCs), and client dropout, optimizing bandwidth allocation and fine-tuning the global...

Jian Tang, Lu-Xi Cheng, Xiu-Hua Li et al. · 0 citations
#federated learning Nov 2026

UOIM: Online Incentive Design for Fair Hierarchical Federated Learning and Unlearning in Edge Computing

Hierarchical Federated Learning (HFL) has emerged as a promising evolution of Federated Learning (FL) where a shared model is learned collaboratively via hierarchical aggregation, and it requires federated unlearning to protect users’ right to be forgotten in the training process. However, most existing designs neglect...

Ying Qian, Lian-Bo Ma, Guo Yu et al. · 0 citations
#federated learning Preprint Oct 2026

FedSSMCoOp: SSM Encoders for light-weight Federated Prompt Learning for Few-shot Classification

Vision-Language Models (VLMs) have shown strong performance across a wide range of downstream vision tasks, thanks to the complementary information contained in the respective domains. Despite the performance gains, most of these approaches rely on aligning these domains using the cosine similarity metric, which fails...

Ankita Das, Ambarish Parthasarathy, S. Channappayya et al. · 0 citations
#federated learning Preprint Oct 2026

Receiver-Domain Behavioral Probing for Backdoor-Resilient Federated GPS Spoofing Detection in UAV Networks

Federated learning lets a UAV fleet train a shared GPS spoofing detector without raw receiver data leaving any aircraft, and several recent UAV-FL designs weight each client by the validation accuracy it reports about itself. We show this self-report is an exploitable attack lever: two compromised clients of ten that p...

Will Jedrzejczak, Cole Walther, D. Gill et al. · 0 citations
#federated learning Book Oct 2026

Deep Learning for Medical Image Analysis: A Comprehensive Review

As deep learning continues to develop at a fast pace, the field of medical imaging has also seen significant changes due to new technologies making it possible to automatically analyse large amounts of complex image data with much greater accuracy and efficiency than ever before. In this chapter, we will describe and e...

Ch V. V. Ramana, S. K. Alla, M. Ratnaraju et al. · 0 citations
#federated learning Review Oct 2026

Artificial Intelligence in Additive Manufacturing: A Critical Review of Methods, Applications and Industrial Challenges

Additive Manufacturing (AM) has grown from a rapid-prototyping tool into a legitimate production technology, yet its broader industrial adoption continues to be constrained by process variability, stochastic defect formation, and the near-impossibility of mapping complex Process Structure Property (PSP) relationships t...

Sarvesh Deshpande, Sahas Walvekar, V. Tiwary et al. · 0 citations
#federated learning Oct 2026

Blockchain and Federated Learning in IoV

The convergence of blockchain and federated learning (FL) will be immensely beneficial for the Internet of Vehicles (IoV) in addressing some of the major concerns such as data security, privacy, scalability, and real-time decision-making. IoV systems architecture inherently creates extensive sensitive data, which is su...

Ramanjeet Singh, Amandeep Kaur, Divneet Singh Kapoor et al. · 0 citations
#federated learning Open access Oct 2026

FedHAD-CrossSilo: artifact of "FedHAD: Enhancing Federated Learning under Non-IID Data Distributions"

Training code of the six compared methods (FedAvg, FedAvgM, FedProx, FedNova, SCAFFOLD and FedHAD), experiment runners, raw per-seed results and telemetry, analysis scripts that reproduce every table and figure of the paper, and the pre-registrations of the control batteries. FedHAD derives each client's number of loca...

Tiago Miranda Linhares, Ahmed Patel, Marcial P. Fernández · 0 citations
#federated learning Oct 2026

Emergency Call Verification Using Cepstral Features With Neural Networks and Federated Learning

ABSTRACT The reliance on vocal communication services, particularly in environments where the security aspects are highly valued such as emergencies, call for reliable methods for that detect the manipulation of environmental sound. One of the current challenges is the detection of SceneFake audio where only the enviro...

Sakshi, Mohit Dua · 0 citations

From tech blogs

See all →
Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

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 Oct 7, 2026

Discovering the value of humanistic inquiry

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.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.