  Federated learning (FL) is a decentralized privacy-preserving learning
technique in which clients learn a joint collaborative model through a central
aggregator without sharing their data. In this setting, all clients learn a
single common predictor (FedAvg), which does not generalize well on each
client's local data due to the statistical data heterogeneity among clients. In
this paper, we address this problem with PersFL, a discrete two-stage
personalized learning algorithm. In the first stage, PersFL finds the optimal
teacher model of each client during the FL training phase. In the second stage,
PersFL distills the useful knowledge from optimal teachers into each user's
local model. The teacher model provides each client with some rich, high-level
representation that a client can easily adapt to its local model, which
overcomes the statistical heterogeneity present at different clients. We
evaluate PersFL on CIFAR-10 and MNIST datasets using three data-splitting
strategies to control the diversity between clients' data distributions. We
empirically show that PersFL outperforms FedAvg and three state-of-the-art
personalization methods, pFedMe, Per-FedAvg, and FedPer on majority data-splits
with minimal communication cost. Further, we study the performance of PersFL on
different distillation objectives, how this performance is affected by the
equitable notion of fairness among clients, and the number of required
communication rounds. PersFL code is available at https://tinyurl.com/hdh5zhxs
for public use and validation.
