  Federated learning (FL) achieves great popularity in the Internet of Things
(IoT) as a powerful interface to offer intelligent services to customers while
maintaining data privacy. Under the orchestration of a server, edge devices
(also called clients in FL) collaboratively train a global deep-learning model
without sharing any local data. Nevertheless, the unequal training
contributions among clients have made FL vulnerable, as clients with heavily
biased datasets can easily compromise FL by sending malicious or heavily biased
parameter updates. Furthermore, the resource shortage issue of edge devices
also becomes a bottleneck. Due to overwhelming computation overheads generated
by training deep-learning models on edge devices, and significant communication
overheads for transmitting deep-learning models across the network, enormous
amounts of resources are consumed in the FL process. This encompasses
computation resources like energy and communication resources like bandwidth.
To comprehensively address these challenges, in this paper, we present FLrce,
an efficient FL framework with a relationship-based client selection and
early-stopping strategy. FLrce accelerates the FL process by selecting clients
with more significant effects, enabling the global model to converge to a high
accuracy in fewer rounds. FLrce also leverages an early stopping mechanism that
terminates FL in advance to save communication and computation resources.
Experiment results show that, compared with existing efficient FL frameworks,
FLrce improves the computation and communication efficiency by at least 47% and
43% respectively.
