arXiv AI June 17, 2026 By Davide Domini, Gianluca Aguzzi, Lorenzo Pellegrini, Mirko Viroli, Lukas Esterle

C2FL: Clustered Continual Federated Learning under Spatial and Temporal Drift

arXiv:2606. 18003v1 Announce Type: cross Abstract: Collective Adaptive Systems (CAS) increasingly rely on machine learning to let each node learn from locally sensed data, aligning its behavior with the surrounding environment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 30

Discovering Collaboration from Novelty: Random Network Distillation for Clustered Federated Learning

arXiv:2606. 30499v1 Announce Type: new Abstract: Federated Learning often suffers under non-independently and identically distributed data, where a single global model may fail to represent the diversity of client distributions.

By Davide Domini, Gianluca Aguzzi, Ivana Dusparic, Danilo Pianini, Mirko Viroli

agents efficiency

arXiv AI

Aug 11

Hierarchical Multi-Task Federated Learning in VANETs

arXiv:2608. 08111v1 Announce Type: cross Abstract: Vehicular Ad hoc Networks (VANETs) increasingly rely on federated learning (FL) to enable collaborative intelligence without sharing raw sensory data.

By M. Saeid HaghighiFard, Sinem Coleri

benchmarks

arXiv Machine Learning

Jul 23

SUM: Unified Geometric Surgery on Spatio-Temporal Adaptation Vectors for Federated Class Incremental Learning

arXiv:2607. 19384v1 Announce Type: new Abstract: Real-world intelligent systems often require both distributed collaboration across data-isolated clients and continual adaptation to evolving tasks.

By Jaeik Kim, Jaeyoung Do

benchmarks

arXiv Computer Vision

2d ago

FedCKA: Representation-Guided Layer Personalization for Federated 3D Perception Across Driving Domains

arXiv:2610.01510v1 Announce Type: new Abstract: Robust perception in intelligent vehicles demands 3D object detectors that remain dependable under domain shifts, such as changes in time of day, locat...

By Jolle Verhoog, Ali Burak \"Unal, Holger Caesar

benchmarks safety

arXiv AI

Aug 13

Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks

arXiv:2608. 11532v1 Announce Type: cross Abstract: In recent research on the Digital Twin-based Vehicular Ad hoc Network(DT-VANET), Federated Learning (FL) has shown its ability to provide data privacy.

By Qasim Zia, Saide Zhu, Haoxin Wang, Zafar Iqbal, Yingshu Li

fine-tuning efficiency safety data-engineering

arXiv AI

Aug 18

Decentralized Federated Learning for Heterogeneous Multi-Task Semantic Communication

arXiv:2608. 15256v1 Announce Type: new Abstract: Collaborative training in distributed semantic communication (DSC) networks typically relies on decentralized federated learning (DFL).

By Lin Yin, Tiejun Lv, Weicai Li, Xi Yu, Xiaoyu He

safety

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