2 papers
cs.NI2025
FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations
Marie Siew, Shikhar Sharma, Zekai Li +5
In edge computing, users' service profiles are migrated due to user mobility. Reinforcement learning (RL) frameworks have been proposed to do so, often trained on simulated data. H…
cs.LG2025
Long-Term Client Selection for Federated Learning with Non-IID Data: A Truthful Auction Approach
Jinghong Tan, Zhian Liu, Kun Guo +1
Federated learning (FL) provides a decentralized framework that enables universal model training through collaborative efforts on mobile nodes, such as smart vehicles in the Intern…