  Traffic steering (TS) is a promising approach to support various service
requirements and enhance transmission reliability by distributing network
traffic loads to appropriate base stations (BSs). In conventional cell-centric
TS strategies, BSs make TS decisions for all user equipment (UEs) in a
centralized manner, which focuses more on the overall performance of the whole
cell, disregarding specific requirements of individual UE. The flourishing
machine learning technologies and evolving UE-centric 5G network architecture
have prompted the emergence of new TS technologies. In this paper, we propose a
knowledge transfer and federated learning-enabled UE-centric (KT-FLUC) TS
framework for highly dynamic 5G radio access networks (RAN). Specifically,
first, we propose an attention-weighted group federated learning scheme. It
enables intelligent UEs to make TS decisions autonomously using local models
and observations, and a global model is defined to coordinate local TS
decisions and share experiences among UEs. Secondly, considering the individual
UE's limited computation and energy resources, a growing and pruning-based
model compression method is introduced, mitigating the computation burden of
UEs and reducing the communication overhead of federated learning. In addition,
we propose a Q-value-based knowledge transfer method to initialize newcomer
UEs, achieving a jump start for their training efficiency. Finally, the
simulations show that our proposed KT-FLUC algorithm can effectively improve
the service quality, achieving 65\% and 38\% lower delay and 52% and 57% higher
throughput compared with cell-based TS and other UE-centric TS strategies,
respectively.
