Analysis and Optimization of Wireless Federated Learning with Data
  Heterogeneity
Xuefeng Han Jun Li Wen Chen Zhen Mei Kang Wei Ming Ding H. Vincent Poor
With the rapid proliferation of smart mobile devices, federated learning (FL)
has been widely considered for application in wireless networks for distributed
model training. However, data heterogeneity, e.g., non-independently
identically distributions and different sizes of training data among clients,
poses major challenges to wireless FL. Limited communication resources
complicate the implementation of fair scheduling which is required for training
on heterogeneous data, and further deteriorate the overall performance. To
address this issue, this paper focuses on performance analysis and optimization
for wireless FL, considering data heterogeneity, combined with wireless
resource allocation. Specifically, we first develop a closed-form expression
for an upper bound on the FL loss function, with a particular emphasis on data
heterogeneity described by a dataset size vector and a data divergence vector.
Then we formulate the loss function minimization problem, under constraints on
long-term energy consumption and latency, and jointly optimize client
scheduling, resource allocation, and the number of local training epochs (CRE).
Next, via the Lyapunov drift technique, we transform the CRE optimization
problem into a series of tractable problems. Extensive experiments on
real-world datasets demonstrate that the proposed algorithm outperforms other
benchmarks in terms of the learning accuracy and energy consumption.