Transformer-Enhanced Task Offloading With Privacy Preservation in Heterogeneous and Dynamic Edge Networks

Abstract

The exponential growth of edge devices and growing demand for low-latency, high-throughput applications have established edge computing as essential infrastructure. Edge computing enables near-data computation to alleviate device burdens, yet task offloading optimization remains challenging due to the heterogeneous, dynamic networks and privacy risks in centralized approaches. To address these intertwined challenges, we propose the federated Q-Learning via Transformer for task offloading (FQTTO) algorithm, which integrates federated learning (FL) with a Transformer-based Q-Network. FL ensures privacy-preserving distributed model updates, while the Transformer employs customized sequential state encodings of task priorities and resource availabilities to autoregressively predict Q-values, incorporating <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href="mei-ieq1-3697303.gif"/></alternatives></inline-formula>-step returns for long-term optimization. Extensive simulations show that compared to the baseline methods, FQTTO achieves average reductions exceeding 20.4% in delay and 22.3% in energy consumption, while improving load balance by at least 13.4% and enhancing the task completion rate by up to 12.1%.