Distributed Subliminal Learning: Replacing Model Updates with Random-Carrier Outputs.

arXiv:2610.05378v1 Announce Type: cross Abstract: Collaborative learning typically exchanges model parameters: federated clients communicate updates, while independently adapted foundation models are combined by exchanging adapters or checkpoints. This makes c