Recently, a considerable amount of works have been made to tackle the
communication burden in federated learning (FL) (e.g., model quantization, data
sparsification, and model compression). However, the existing methods, that
boost the communication efficiency in FL, result in a considerable trade-off
between communication efficiency and global convergence rate. We formulate an
optimization problem for compression-aided FL, which captures the relationship
between the distortion rate, number of participating IoT devices, and
convergence rate. Following that, the objective function is to minimize the
total transmission time for FL convergence. Because the problem is non-convex,
we propose to decompose it into sub-problems. Based on the property of a FL
model, we first determine the number of IoT devices participating in the FL
process. Then, the communication between IoT devices and the server is
optimized by efficiently allocating wireless resources based on a coalition
game. Our theoretical analysis shows that, by actively controlling the number
of participating IoT devices, we can avoid the training divergence of
compression-aided FL while maintaining the communication efficiency.

提出基于压缩辅助的联邦学习问题，通过将其分解为子问题并基于协作博弈来有效分配无线资源，从而优化了 IoT 设备和服务器之间的通信，通过积极控制参与学习的设备数量，既可避免训练发散又能保持通信效率。