Improved Generalization Bounds for Communication Efficient Federated
  Learning
Peyman Gholami Hulya Seferoglu
This paper focuses on reducing the communication cost of federated learning
by exploring generalization bounds and representation learning. We first
characterize a tighter generalization bound for one-round federated learning
based on local clients' generalizations and heterogeneity of data distribution
(non-iid scenario). We also characterize a generalization bound in R-round
federated learning and its relation to the number of local updates (local
stochastic gradient descents (SGDs)). Then, based on our generalization bound
analysis and our representation learning interpretation of this analysis, we
show for the first time that less frequent aggregations, hence more local
updates, for the representation extractor (usually corresponds to initial
layers) leads to the creation of more generalizable models, particularly for
non-iid scenarios. We design a novel Federated Learning with Adaptive Local
Steps (FedALS) algorithm based on our generalization bound and representation
learning analysis. FedALS employs varying aggregation frequencies for different
parts of the model, so reduces the communication cost. The paper is followed
with experimental results showing the effectiveness of FedALS.