  Federated Learning (FL) over wireless network enables data-conscious services
by leveraging the ubiquitous intelligence at network edge for
privacy-preserving model training. As the proliferation of context-aware
services, the diversified personal preferences causes disagreeing conditional
distributions among user data, which leads to poor inference performance. In
this sense, clustered federated learning is proposed to group user devices with
similar preference and provide each cluster with a personalized model. This
calls for innovative design in edge association that involves user clustering
and also resource management optimization. We formulate an accuracy-cost
trade-off optimization problem by jointly considering model accuracy,
communication resource allocation and energy consumption. To comply with
parameter encryption techniques in FL, we propose an iterative solution
procedure which employs deep reinforcement learning based approach at cloud
server for edge association. The reward function consists of minimized energy
consumption at each base station and the averaged model accuracy of all users.
Under our proposed solution, multiple edge base station are fully exploited to
realize cost efficient personalized federated learning without any prior
knowledge on model parameters. Simulation results show that our proposed
strategy outperforms existing strategies in achieving accurate learning at low
energy cost.
