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ORCIDFedEdge-AI@MobiCom 2025: Hong Kong, SAR, China
- Proceedings of the Federated Learning and Edge AI for Privacy and Mobility, FLEdge-AI 2025, Hong Kong, SAR, China, November 4-8, 2025. ACM 2025, ISBN 979-8-4007-1976-9
- Liang Zhao
, Yiwen Zhang, Sinong Zhang, Chaojie Gu, Yujun Yao, Victor C. M. Leung:
Collaborative Offloading and 3D Trajectory Design in Multi-UAV-assisted MEC Networks for Emergency Rescue. 1-3 - Shan-cang Li, Shanshan Zhao, Yongjian Ding:
FedPETv1: Edge-Aware Federated Learning with Differential Privacy for Distributed Medical Diagnostics. 4-7 - Hanxiao Shi, Qianru Wang, Qingyang Li, Hui Li, Jiangtao Cui:
FLATTN: Bridging Temporal Resolution Disparities via Attention Alignment in Federated Distillation for Human Activity Recognition. 8-15 - Lingyu Qiu, Daniela Annunziata, Fabio Giampaolo, Francesco Piccialli:
Deciphering One-Shot Federated Learning: The Pivotal Role of Pretrained Models. 16-22 - Pi-Wei Chen, Naveen Sharma, Rafal Cupek, Marek Drewniak, Zih-Ching Chen, Jerry Chun-Wei Lin:
FedAGV: Federated Meta-Learning for Rapid Adaptation of AGV Trajectory Planing. 23-29 - Gianluca Dalmasso, Pedro Porto Buarque de Gusmão, Attilio Fiandrotti, Marco Grangetto:
Efficient Federated Model Aggregation through Neural Velocity. 30-36 - Eunju Jeong, Dong-oh Kang:
AROM: Control-Theoretic Learning for Resilient and Decentralized Edge Intelligence. 37-43 - Dariusz Marek, Jakub Szygula, Piotr Biernacki, Marcin Paszkuta, Adam Domanski, Marcin Fojcik, Marta Szczygiel, Konrad Wojciechowski:
Neural Tuning of Parameters in a Collision Avoidance Mechanism for Swarms of Drones. 44-51 - Joyce Yuan, Brian Le, Kathryn Le, Yichuan Shi, Abhishek Singh, Rishi Sharma, Angel Patricio, Ramesh Raskar:
SONAR Web: A Platform-Agnostic Framework for Real-Time Decentralized Learning Across Heterogeneous Edge Clients. 52-58 - Yui Yamashita, Akihito Taya, Yoshito Tobe:
Robust Inference under Modality Missing in Multimodal Split Learning. 59-65 - Christoph Düsing, Philipp Cimiano:
Towards Lightweight Input Control for Cross-Device Federated Learning With Dynamic Participation. 66-69 - Pham Duy Thanh, Tran Anh Khoa, Minh-Son Dao, Koji Zettsu:
Scalable Federated Split Learning for Smart Mobile and IoT Devices. 70-76 - Navidreza Asadi, Halil Ibrahim Bengu, Lars Wulfert, Hendrik Wöhrle, Wolfgang Kellerer:
Gist - Optimizing Segmentation for Decentralized Federated Learning on Tiny Devices. 77-84 - Yinan Zhang, Jiannong Cao, Mingjin Zhang, Ruosong Yang:
FedHO: Memory-Efficient Federated Fine-Tuning for Large Models via Hybrid Gradient Computation. 85-92
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