  Federated Learning (FL) is a collaborative learning performed by a moderator
that protects data privacy. Existing cross-silo FL solutions seldom address the
absence of participating clients during training which can seriously degrade
model performances, particularly for unbalanced and non-IID client data. We
address this issue by generating secure data digests from the raw data and
using them to guide model training at the FL moderator. The proposed FL with
data digest (FedDig) framework can tolerate unexpected client absence while
preserving data privacy. This is achieved by de-identifying digests by mixing
and perturbing the encoded features of the raw data in the feature space. The
feature perturbing is performed following the Laplace mechanism of Differential
Privacy. We evaluate FedDig on EMNIST, CIFAR-10, and CIFAR-100 datasets. The
results consistently outperform three baseline algorithms (FedAvg, FedProx, and
FedNova) by large margins in multiple client absence scenarios.
