Comments on "VERSA: Verifiable Secure Aggregation for Cross-Device Federated Learning"

Recently, in IEEE Transactions on Dependable and Secure Computing (TDSC), the VERSA scheme proposed by Hahn et al. uses a double aggregation method for verifying the correctness of results returned from the server. The authors proposed that the correctness of the model aggregation can be verified with lower verification overhead by utilizing only a lightweight pseudorandom generator. To support verifiability of results returned from the server, a method of sharing a pair of vectors (a,b) by all clients is proposed, which is one of the most important work in VERSA. Unfortunately, in this paper, we show that the method is incorrect, which leads clients to consistently conclude that the aggregated results are incorrect. Furthermore, the model training process in federated learning is forced to abort. Finally, we demonstrate our view through theory analysis and instantiation verification.