Research on the optimisation of communication efficiency based on adaptive improved federated learning
Year: 2025
Type: article
Abstract: Aiming at the communication efficiency bottleneck in the internet of things and edge computing scenarios, this paper proposes a communication efficiency improvement scheme based on adaptive improved federated learning. By constructing an ARMA bandwidth prediction model enhanced by wavelet transform, the client network environment is predicted, and the improved Sketch compression algorithm is adopted to dynamically adapt to the real-time bandwidth conditions, thus the communication efficiency optimisation in the internet of things and edge computing scenarios is achieved. Experiments show that the accuracy of the proposed method researches 95%, the average uplink communication time is 0.5 seconds, and the communication efficiency exceeds 1.7. It provides key technical support for real-time federated learning deployment in 5G edge computing environment.
Authors: Xuefei Zhang, Yanli Zhao
Institution: Shanxi Engineering Vocational College
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411455590