The paper introduces FedQoS, an asynchronous federated learning framework designed for multimodal in‑cabin interaction in smart vehicles. It uses a two‑phase gating mechanism: a resource‑aware training gate that starts local learning only when sensing buffers and energy reserves meet safety thresholds, and a QoS‑aware transmission policy that gates uplink updates based on an efficiency score balancing model novelty, latency, and energy costs. Experiments on vehicular datasets show FedQoS achieves competitive personalized accuracy with only marginal loss compared to FedAvg, while reducing communication overhead by 76.7% and latency cost by 26.0%.
By Baran Can G\"ul, Mert Nak{\i}p, Nasser Jazdi, Michael Weyrich
arXiv:2412.06210v3 Announce Type: replace
Abstract: With the rapid development of the Internet of Things (IoT), federated learning (FL) has gained increasing attention for its privacy-preserving use...
By Jiechao Gao, Yuangang Li, Jie Wang, Yue Zhao, Michael Lepech, Brad Campbell
The paper introduces MiLAAP, an attention‑based framework that predicts channel occupancy and node motion in channel‑hopping communication systems without exchanging state information. By leveraging self‑attention and multi‑head attention, each node passively observes local channel activity to forecast interference patterns and mobility, achieving near‑perfect prediction accuracy across varied mobility scenarios and demonstrating zero‑shot generalizability to new channel‑sequence periods.
By Yung-Fu Chen, Anish Arora
arXiv:2609.15763v1 Announce Type: cross
Abstract: Federated continual learning (FCL) enables shared global models to continuously adapt to distributed and non-stationary data streams, making it impor...
By Yuxuan Sun, Yuxuan Bai, Tan Chen, Sheng Zhou, Zhisheng Niu
arXiv:2607. 16930v1 Announce Type: cross Abstract: Throughput prediction is foundational for artificial intelligence-driven 6G resource orchestration.
By Muhammad Kabeer, Rosdiadee Nordin, Nadiva Nuriftitah, Sian Lun Lau
The paper introduces COMLLM, a generative framework that combines Group Relative Policy Optimization with a Look‑Ahead Collaborative Simulation to enable multi‑turn reasoning for task offloading in Mobile Edge Computing. By performing multi‑step Monte Carlo rollouts that jointly model server queue dynamics, COMLLM incorporates long‑term system evolution into its reward design, achieving near‑optimal latency and improved load‑balancing fairness. The framework demonstrates zero‑shot scalability to larger network topologies, outperforming supervised fine‑tuning, deep reinforcement learning, and heuristic baselines without requiring retraining.
By Ning Yang, Chuangxin Cheng, Haijun Zhang
arXiv:2606. 03664v1 Announce Type: cross Abstract: Ultra Reliable and Low Latency Communications (URLLC) was one of the main motivations behind 5G, with 3GPP advertising 1-10 ms latency targets for applications such as industrial automation, Vehicle-To-Everything (V2X), tactical edge networking, and unmanned-system control.
By Maxime Elkael, Michele Polese, Yunseong Lee, Koichiro Furueda, Tommaso Melodia
arXiv:2603. 16867v2 Announce Type: replace Abstract: Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but their verbose reasoning traces and large context requirements make them impractical for edge deployment.
By Yelysei Bondarenko, Thomas Hehn, Rob Hesselink, Romain Lepert, Fabio Valerio Massoli, Evgeny Mironov, Leyla Mirvakhabova, Tribhuvanesh Orekondy, Spyridon Stasis, Andrey Kuzmin, Anna Kuzina, Markus Nagel, Ankita Nayak, Corrado Rainone, Ork de Rooij, Paul N Whatmough, Arash Behboodi, Babak Ehteshami Bejnordi
arXiv:2607. 14176v1 Announce Type: new Abstract: Reliable, low-latency uplink connectivity is a key requirement for C-V2X networks in dense urban environments, where fast channel variations and blockages often degrade direct vehicle-to-infrastructure links.
By Giambattista Amati, Federica Mangiatordi, Emiliano Pallotti, Simone Angelini, Pierpaolo Salvo, Paola Vocca
Federated fine‑tuning of large language models (LLMs) over mobile radio access networks (RANs) creates recurring, deadline‑sensitive communication workloads that are currently treated as independent device‑originated flows by transport networks. This mismatch hinders efficient resource provisioning, especially for optical circuit switching and all‑photonics transport that require predictable traffic. The paper proposes that future RANs act as learning‑aware traffic shapers, aggregating asynchronous user‑equipment updates at the gNB to produce fewer, bounded‑size transfers that can be scheduled on dynamically provisioned optical links, thereby combining packet‑based access flexibility with programmable transport capacity.
By Emilio Paolini, Andrea Pinto, Flavio Esposito, Luca Valcarenghi
FedSAP is a federated learning framework that addresses heterogeneous edge devices by using structured pruning as a budget-constrained tri-state channel allocation. It partitions model channels into a Global pool, pseudo-domain-specific Private pools, and a Dropped state, allowing broadly useful features to be shared while isolating domain-sensitive updates. Experiments on Digits and Office-Caltech datasets show FedSAP achieving higher mean global accuracy than the strongest baseline while supporting up to 80% client pruning ratios.
By Wentao Yue, Tianyou Lai, Hongji Li, Qingyu Mao, Qilei Li
arXiv:2502. 11007v5 Announce Type: replace Abstract: Compared to traditional machine learning models, recent large language models (LLMs) can exhibit multi-task-solving capabilities through multi-modal data sources and multi-turn conversations.
By Liangqi Yuan, Dong-Jun Han, Shiqiang Wang, Christopher G. Brinton