  In this work, we propose FLVoogd, an updated federated learning method in
which servers and clients collaboratively eliminate Byzantine attacks while
preserving privacy. In particular, servers use automatic Density-based Spatial
Clustering of Applications with Noise (DBSCAN) combined with S2PC to cluster
the benign majority without acquiring sensitive personal information.
Meanwhile, clients build dual models and perform test-based distance
controlling to adjust their local models toward the global one to achieve
personalizing. Our framework is automatic and adaptive that servers/clients
don't need to tune the parameters during the training. In addition, our
framework leverages Secure Multi-party Computation (SMPC) operations, including
multiplications, additions, and comparison, where costly operations, like
division and square root, are not required. Evaluations are carried out on some
conventional datasets from the image classification field. The result shows
that FLVoogd can effectively reject malicious uploads in most scenarios;
meanwhile, it avoids data leakage from the server-side.
