Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled
  Realization for Multi-Round Secure Aggregation in Federated Learning
Kaiping Cui Xia Feng Liangmin Wang Haiqin Wu Xiaoyu Zhang Boris Düdder
Secure aggregation enables federated learning (FL) to perform collaborative
training of clients from local gradient updates without exposing raw data.
However, existing secure aggregation schemes inevitably perform an expensive
fresh setup per round because each client needs to establish fresh
input-independent secrets over different rounds. The latest research, Flamingo
(S&P 2023), designed a share-transfer-based reusable secret key to support the
server continuously performing multiple rounds of aggregation. Nevertheless,
the share transfer mechanism it proposed can only be achieved with P
probability, which has limited reliability. To tackle the aforementioned
problems, we propose a more reliable and anonymously authenticated scheme
called Chu-ko-nu for multi-round secure aggregation. Specifically, in terms of
share transfer, Chu-ko-nu breaks the probability P barrier by supplementing a
redistribution process of secret key components (the sum of all components is
the secret key), thus ensuring the reusability of the secret key. Based on this
reusable secret key, Chu-ko-nu can efficiently perform consecutive aggregation
in the following rounds. Furthermore, considering the client identity
authentication and privacy protection issue most approaches ignore, Chu-ko-nu
introduces a zero-knowledge proof-based authentication mechanism. It can
support clients anonymously participating in FL training and enables the server
to authenticate clients effectively in the presence of various attacks.
Rigorous security proofs and extensive experiments demonstrated that Chu-ko-nu
can provide reliable and anonymously authenticated aggregation for FL with low
aggregation costs, at least a 21.02% reduction compared to the state-of-the-art
schemes.