Federated Model-Agnostic Meta-Learning With Sharpness-Aware Minimization for Internet of Things Optimization

Federated meta-learning (ML) is a promising optimization framework for the intelligent Internet of Things (IoT). However, the generalization ability of existing federated ML is limited because it is a bilayer structure, which has a more complex loss landscape. Moreover, the loss landscape of bilevel optimization has more saddle points and sharp points, which may lead to different generalization performances. Therefore, how to choose an optimal point is crucial for improving the generalization ability of federated ML. For this reason, this article proposes a provable federated ML algorithm by using the sharpness-aware minimization technique, referred to as FedAvg-sharp-MAML (FSM). Furthermore, we rigorously analyse the convergence and generalization bound of FSM. Specifically, when local iteration rounds
$T=1$
, the rate of
$O(1/K)$
can be achieved, where K is the number of global iterations. Furthermore, this rate can match the Per-Fedavg method. Meanwhile, we achieve a better generalization bound than the state of the art federated ML, where PAC-Bayesian generalization bounds are introduced in our analysis. Finally, we conduct some experiments to verify the performance of FSM. The experimental results show that the FSM has good generalization performance compared to the existing federated ML algorithms.