  Federated learning enables multiple data owners to jointly train a machine
learning model without revealing their private datasets. However, a malicious
aggregation server might use the model parameters to derive sensitive
information about the training dataset used. To address such leakage,
differential privacy and cryptographic techniques have been investigated in
prior work, but these often result in large communication overheads or impact
model performance. To mitigate this centralization of power, we propose SCOTCH,
a decentralized m-party secure-computation framework for federated aggregation
that deploys MPC primitives, such as secret sharing. Our protocol is simple,
efficient, and provides strict privacy guarantees against curious aggregators
or colluding data-owners with minimal communication overheads compared to other
existing state-of-the-art privacy-preserving federated learning frameworks. We
evaluate our framework by performing extensive experiments on multiple datasets
with promising results. SCOTCH can train the standard MLP NN with the training
dataset split amongst 3 participating users and 3 aggregating servers with
96.57% accuracy on MNIST, and 98.40% accuracy on the Extended MNIST (digits)
dataset, while providing various optimizations.
