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federated learning

2,055 papers

#reinforcement learning Open access Oct 2026

Governance-aware autonomous retail coordination in artificial intelligence cities using multi-agent reinforcement learning, blockchain accountability, and federated learning

When autonomous systems take operational authority over urban commerce, accountability and human oversight matter as much as efficiency. We present AAIRM, a governance-aware autonomous retail coordination framework addressing three requirements for trustworthy procurement: tamper-evident decision provenance, data sover...

Toqeer Ali Syed, Ali Akarma, Shahid Kamal et al. · 1 citation
#federated learning Nov 2026

Energy-Efficient Offloading, Caching, and Resource Allocation for Blockchain-Assisted Low-Altitude Flying Networks: An Integrated Federated Learning and MAPPO Approach

In 6G low-altitude edge intelligent networks, the proliferation of delay-sensitive Internet of Things (IoT) services exacerbates mutual interference among IoT devices, thereby reducing the efficiency of resource allocation. To address this challenge, we propose a blockchain-assisted federated learning (FL)-based four-l...

Zhi-Ran Wang, Bin-Tao Hu, Miguel López-Benítez et al. · 0 citations
#federated learning Nov 2026

FedPKD+: A Prototype-Based Framework for Efficient and Flexible Heterogeneous Federated Learning

Federated Learning (FL) enables collaborative model training across mobile and edge devices without sharing raw data, but its deployment is hindered by <italic>system heterogeneity</italic> and <italic>non-IID</italic> data. Existing FL methods either require homogeneous architectures or suffer from accuracy loss and h...

Tong Liu, Feng Lyu, Shucheng Li et al. · 0 citations
#federated learning Nov 2026

FedTuneFM: Federated Fine-Tuning of Foundation Models for Mobile Edge Computing via Adaptive Compression and Attention Alignment

Federated Learning (FL) has emerged as a promising paradigm for fine-tuning large-scale Foundation Models (FMs) in distributed environments while preserving data privacy. However, efficiently adapting FMs in FL remains challenging, especially in mobile edge computing scenarios where devices are resource-constrained and...

Ya-Lan Jiang, Bin Song · 0 citations
#federated learning Nov 2026

OFDM-Assisted Over-the-Air Computation With Privacy Protection in Federated Learning

Federated Learning (FL) is an emerging distributed machine learning paradigm that protects data privacy by performing iterative local training and gradient aggregation across multiple devices and a central server. Over-the-air computation enables fast aggregation when multiple devices need to upload their local gradien...

Shi-Yuan Zuo, Rong-Fei Fan, Pu-Ning Zhao et al. · 0 citations
#federated learning Nov 2026

Transmission-Efficient Federated Learning via Chessboard Convolutional Kernel

Reducing the communication cost to alleviate the contradiction between the limited bandwidth and huge transmitted parameters in Federated Learning (FL) is a persisting challenge. Since the transmission parameters obtained by vanilla convolution primarily rely on the product of kernel and channels, recent works (e.g., F...

Wei Li, Wen-Hao Li, Chao Ma et al. · 0 citations
#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

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MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

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