Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning

ArXi:2605.08616v1 Announce Type: new Collaborative machine learning (CML) enables multiple clients to train a global model jointly in a data-distributed setting. To address data privacy and communication efficiency, one-shot CML has been increasingly adopted, where clients communicate with the server only once by sharing synthetic or processed proxy data. This single-round communication, however, eliminates the possibility of iterative correction at the server, making the learning process particularly vulnerable to client unreliability.