We present Sphinx, a privacy-preserving online learning system that strikes a balance between model performance, computational efficiency, and privacy preservation. At its core, Sphinx combines homomorphic encryption and differential privacy reciprocally to maintain the model with most of its parameters as plaintexts, enabling fast training and inference protocol designs.

We propose a comprehensive evaluation framework for FL systems. Specifically, we first introduce the PRACT model, which defines five metrics that cannot be excluded from FL evaluation, including Privacy, Robustness, Accuracy, Communication, and Time efficiency. Then we design and implement a benchmarking system called FedEval, which enables the systematic evaluation and comparison of existing works under consistent experimental conditions.

We present DLCP, a network substrate that speeds up DNN training by fully embracing several unique characteristics of deep learning. Compared to prior work in this space, DLCP integrates three simple-yet-effective techniques to form a multi-layered protection against long tail latency caused by transient packet drops and queueing.