Encrypted Data Caching and Learning Framework for Robust Federated
Learning-based Mobile Edge Computing
Abstract
Federated Learning (FL) plays a pivotal role in enabling artificial
intelligence (AI)-based mobile applications in mobile edge computing
(MEC). However, due to the resource heterogeneity among participating
mobile users (MUs), delayed updates from slow MUs may deteriorate the
learning speed of the MEC-based FL system, commonly referred to as the
straggling problem. To tackle the problem, this work proposes a novel
privacy-preserving FL framework that utilizes homomorphic encryption
(HE) based solutions to enable MUs, particularly resource-constrained
MUs, to securely offload part of their training tasks to the cloud
server (CS) and mobile edge nodes (MENs). Our framework first develops
an efficient method for packing batches of training data into HE
ciphertexts to reduce the complexity of HE-encrypted training at the
MENs/CS. On that basis, the mobile service provider (MSP) can
incentivize straggling MUs to encrypt part of their local datasets that
are uploaded to certain MENs or the CS for caching and remote training.
However, caching a large amount of encrypted data at the MENs and CS for
FL may not only overburden those nodes but also incur a prohibitive cost
of remote training, which ultimately reduces the MSP’s overall profit.
To optimize the portion of MUs’ data to be encrypted, cached, and
trained at the MENs/CS, we formulate an MSP’s profit maximization
problem, considering all MUs’ and MENs’ resource capabilities and data
handling costs (including encryption, caching, and training) as well as
the MSP’s incentive budget. We then show that the problem is convex and
can be efficiently solved using an interior point method. Extensive
simulations on a real-world human activity recognition dataset show that
our proposed framework can achieve much higher model accuracy (improving
up to 24.29%) and faster convergence rate (by 2.86 times) than those of
the conventional FedAvg approach when the straggling probability varies
between 20% and 80%. Moreover, the proposed framework can improve the
MSP’s profit up to 2.84 times compared with other baseline FL approaches
without MEN-assisted training.