Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Design
arXiv:2410.05662v4 Announce Type: replace Abstract: Most federated learning (FL) approaches assume a fixed device set. However, real-world scenarios often involve devices dynamically joining or leaving the system, driven by, e.g., user mobility patterns or handovers across cell boundaries. This dynamic setting introduces unique challenges: (1) the optimization objective evolves with the active device set, unlike traditional FL's static objective; and (2) the current global model may no longer serve as an effective initialization for subsequent rounds, potentially hindering adaptation, delaying
This research addresses a critical challenge in federated learning as real-world applications increasingly demand robust systems accommodating dynamic device populations.
Federated learning's practical utility hinges on its ability to handle volatile network conditions and device participation, making advancements in this area crucial for broader adoption.
The development of communication-efficient algorithms under dynamic conditions will enable more resilient and scalable federated learning deployments in edge computing environments.
- · Edge AI developers
- · Mobile device manufacturers
- · Distributed computing platforms
- · Centralized AI training models
- · Inefficient FL algorithms
Improved performance and reliability of federated learning in real-world scenarios with dynamic device sets.
Accelerated adoption of federated learning across industries like IoT, healthcare, and automotive due to enhanced practicality.
Shift towards more privacy-preserving and device-centric AI models, reducing reliance on centralized data collection.
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Read at arXiv cs.LG