  Federated learning (FL) facilitates collaborative training of machine
learning models among a large number of clients while safeguarding the privacy
of their local datasets. However, FL remains susceptible to vulnerabilities
such as privacy inference and inversion attacks. Single-server secure
aggregation schemes were proposed to address these threats. Nonetheless, they
encounter practical constraints due to their round and communication
complexities. This work introduces Fluent, a round and communication-efficient
secure aggregation scheme for private FL. Fluent has several improvements
compared to state-of-the-art solutions like Bell et al. (CCS 2020) and Ma et
al. (SP 2023): (1) it eliminates frequent handshakes and secret sharing
operations by efficiently reusing the shares across multiple training
iterations without leaking any private information; (2) it accomplishes both
the consistency check and gradient unmasking in one logical step, thereby
reducing another round of communication. With these innovations, Fluent
achieves the fewest communication rounds (i.e., two in the collection phase) in
the malicious server setting, in contrast to at least three rounds in existing
schemes. This significantly minimizes the latency for geographically
distributed clients; (3) Fluent also introduces Fluent-Dynamic with a
participant selection algorithm and an alternative secret sharing scheme. This
can facilitate dynamic client joining and enhance the system flexibility and
scalability. We implemented Fluent and compared it with existing solutions.
Experimental results show that Fluent improves the computational cost by at
least 75% and communication overhead by at least 25% for normal clients. Fluent
also reduces the communication overhead for the server at the expense of a
marginal increase in computational cost.
