# SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees

arXiv: https://arxiv.org/abs/2202.04169 · HTML: https://mlsanity.com/paper/2202.04169/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Practical Secure Aggregation for Federated Learning on User-Held Data](https://mlsanity.com/paper/1611.04482/) (2016)
- [Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning](https://mlsanity.com/paper/2002.04156/) (2020)
- [LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2109.14236/) (2021)
- [FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2009.11248/) (2020)
- [Communication-Computation Efficient Secure Aggregation for Federated Learning](https://mlsanity.com/paper/2012.05433/) (2020)

## Built on by (most cited first, 14 of 14)

- [A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency](https://mlsanity.com/paper/2307.10655/) (2023)
- [SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning](https://mlsanity.com/paper/2203.13060/) (2022)
- [FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models](https://mlsanity.com/paper/2304.13407/) (2023)
- [FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2305.05506/) (2023)
- [The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape](https://mlsanity.com/paper/2405.03636/) (2024)
- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [Federated Analytics: A survey](https://mlsanity.com/paper/2302.01326/) (2023)
- [Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association](https://mlsanity.com/paper/2503.04564/) (2025)
- [Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusion](https://mlsanity.com/paper/2306.14088/) (2023)
- [ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment](https://mlsanity.com/paper/2302.09913/) (2023)
- [Hierarchical Secure Aggregation with Heterogeneous Security Constraints and Arbitrary User Collusion](https://mlsanity.com/paper/2507.14768/) (2025)
- [Information-Theoretic Decentralized Secure Aggregation with Passive Collusion Resilience](https://mlsanity.com/paper/2508.00596/) (2025)
- [Distilling A Universal Expert from Clustered Federated Learning](https://mlsanity.com/paper/2506.20285/) (2025)
- [Secure Embedding Aggregation for Federated Representation Learning](https://mlsanity.com/paper/2206.09097/) (2022)

## Abstract (arXiv)

We propose SwiftAgg, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of N distributed users, each of size L, trained on their local data, in a privacy-preserving manner. Compared with state-of-the-art secure aggregation protocols, SwiftAgg significantly reduces the communication overheads without any compromise on security. Specifically, in presence of at most D dropout users, SwiftAgg achieves a users-to-server communication load of (T+1)L and a users-to-users communication load of up to (N-1)(T+D+1)L, with a worst-case information-theoretic security guarantee, against any subset of up to T semi-honest users who may also collude with the curious server. The key idea of SwiftAgg is to partition the users into groups of size D+T+1, then in the first phase, secret sharing and aggregation of the individual models are performed within each group, and then in the second phase, model aggregation is performed on D+T+1 sequences of users across the groups. If a user in a sequence drops out in the second phase, the rest of the sequence remain silent. This design allows only a subset of users to communicate with each other, and only the users in a single group to directly communicate with the server, eliminating the requirements of 1) all-to-all communication network across users; and 2) all users communicating with the server, for other secure aggregation protocols. This helps to substantially slash the communication costs of the system.