  Federated learning (FL), an attractive and promising distributed machine
learning paradigm, has sparked extensive interest in exploiting tremendous data
stored on ubiquitous mobile devices. However, conventional FL suffers severely
from resource heterogeneity, as clients with weak computational and
communication capability may be unable to complete local training using the
same local training hyper-parameters. In this paper, we propose Dap-FL, a deep
deterministic policy gradient (DDPG)-assisted adaptive FL system, in which
local learning rates and local training epochs are adaptively adjusted by all
resource-heterogeneous clients through locally deployed DDPG-assisted adaptive
hyper-parameter selection schemes. Particularly, the rationality of the
proposed hyper-parameter selection scheme is confirmed through rigorous
mathematical proof. Besides, due to the thoughtlessness of security
consideration of adaptive FL systems in previous studies, we introduce the
Paillier cryptosystem to aggregate local models in a secure and
privacy-preserving manner. Rigorous analyses show that the proposed Dap-FL
system could guarantee the security of clients' private local models against
chosen-plaintext attacks and chosen-message attacks in a widely used
honest-but-curious participants and active adversaries security model. In
addition, through ingenious and extensive experiments, the proposed Dap-FL
achieves higher global model prediction accuracy and faster convergence rates
than conventional FL, and the comprehensiveness of the adjusted local training
hyper-parameters is validated. More importantly, experimental results also show
that the proposed Dap-FL achieves higher model prediction accuracy than two
state-of-the-art RL-assisted FL methods, i.e., 6.03% higher than DDPG-based FL
and 7.85% higher than DQN-based FL.
