Paper 2026/1335

Falafel: Modular Zero-Knowledge Proofs of Training in the Federated Setting

Abstract

We introduce Falafel, a modular scheme for Fast, Authenticated, Locally Attested FEderated Learning, with which parties can create a zero-knowledge proof of training (zkPoT) for Federated Learning (FL). The proof guarantees active security during the federated training process as well as publicly verifiable correctness of the final, trained model. All without revealing any additional information about the local datasets or intermediate local model states. Specifically, our approach targets FL of (deep) neural networks with a centralized server for weight updates. Our zkPoT not only offers attestation for local training steps, but also for the centralized weight update, as well as taking into account input authenticity by introducing a trusted auditor. This way an external verifier can check the entire training process, from dataset to final model. In contrast to prior work on zkPoTs, our construction solely relies on well-understood cryptographic assumptions and primitives, is highly parallelizable, and takes a modular approach. This modular (commit-and-prove) approach uses several novel core proof components, that could be swapped for other building blocks if desired. We show that, for LeNet, we generate a zkPoT of 70 kB in roughly 150 seconds for a single training round. Falafel’s prover time is in line with prior work, and its proof size is significantly smaller (10–15×), without relying on less-understood assumptions or instantiating Fiat–Shamir using arithmetic hash functions.