  Federated Learning (FL) is a distributed learning paradigm that enables
mutually untrusting clients to collaboratively train a common machine learning
model. Client data privacy is paramount in FL. At the same time, the model must
be protected from poisoning attacks from adversarial clients. Existing
solutions address these two problems in isolation. We present FedPerm, a new FL
algorithm that addresses both these problems by combining a novel intra-model
parameter shuffling technique that amplifies data privacy, with Private
Information Retrieval (PIR) based techniques that permit cryptographic
aggregation of clients' model updates. The combination of these techniques
further helps the federation server constrain parameter updates from clients so
as to curtail effects of model poisoning attacks by adversarial clients. We
further present FedPerm's unique hyperparameters that can be used effectively
to trade off computation overheads with model utility. Our empirical evaluation
on the MNIST dataset demonstrates FedPerm's effectiveness over existing
Differential Privacy (DP) enforcement solutions in FL.
