A shift design that maximizes the eavesdropper's final model error under a power constraint is derived for federated learning, with simulations showing better privacy than ModShift at 24% of its power.
Advances and open problems in federated learning,
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MaxModShift: Model Privacy via Designed Shifts
A shift design that maximizes the eavesdropper's final model error under a power constraint is derived for federated learning, with simulations showing better privacy than ModShift at 24% of its power.