Data/Information security and privacy are increasingly important as ML methods become more powerful. Federated Learning (FL) serves as a method for

1. protecting data security and privacy by keeping data localized
2. parallelizing large-scale models through a divide-and-conquer approach
   

For FL algorithm efficiency, the Alternating Direction Method of Multipliers (ADMM) is a commonly used distributed/consensus optimization method for collaborative model training. Despite the data availability, FL also faces the following issues:

• Data distribution across clients: if data is distributed non-identically across nodes, it could negatively impact model convergence
• Privacy in model transmission: membership inference attacks, model inversion attacks
• Robustness in training: data poisoning attacks, Byzantine attacks, backdoor attacks

We work on FL schemes specifically to address these malicious attacks and to enhance efficiency and robustness of the training process.