# SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning arXiv: https://arxiv.org/abs/2203.13060 · HTML: https://mlsanity.com/paper/2203.13060/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Builds on 14 of its 21 references that are in the map. - First posted on arXiv 24 Mar 2022. ## Builds on (most cited first, 14 of 14) - [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) - [Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy](https://mlsanity.com/paper/1806.00939/) (2018) - [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) - [CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2112.08909/) (2021) - [Information Theoretic Secure Aggregation with User Dropouts](https://mlsanity.com/paper/2101.07750/) (2021) - [Communication-Computation Efficient Secure Aggregation for Federated Learning](https://mlsanity.com/paper/2012.05433/) (2020) - [SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees](https://mlsanity.com/paper/2202.04169/) (2022) ## Built on by (most cited first, 18 of 18) - [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) - [DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing](https://mlsanity.com/paper/2210.02680/) (2022) - [FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models](https://mlsanity.com/paper/2304.13407/) (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) - [Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization](https://mlsanity.com/paper/2406.14362/) (2024) - [Survey of Federated Learning Models for Spatial-Temporal Mobility Applications](https://mlsanity.com/paper/2305.05257/) (2023) - [Information-Theoretic Decentralized Secure Aggregation with Passive Collusion Resilience](https://mlsanity.com/paper/2508.00596/) (2025) - [Generalized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff for Resilient, Secure, and Private Computation](https://mlsanity.com/paper/2204.11168/) (2022) - [Secure Embedding Aggregation for Federated Representation Learning](https://mlsanity.com/paper/2206.09097/) (2022) - [A Blockchain Solution for Collaborative Machine Learning over IoT](https://mlsanity.com/paper/2311.14136/) (2023) - [Federated One-Shot Learning with Data Privacy and Objective-Hiding](https://mlsanity.com/paper/2504.21182/) (2025) - [Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning](https://mlsanity.com/paper/2506.09870/) (2025) - [EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning](https://mlsanity.com/paper/2506.13612/) (2025) - [Beyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated Learning](https://mlsanity.com/paper/2508.12978/) (2025) - [The Capacity of Information-Theoretic Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2606.07277/) (2026) - [Secure Aggregation with Top-K Sparsification in Decentralized Federated Learning](https://mlsanity.com/paper/2606.10780/) (2026) ## Abstract (arXiv) We propose SwiftAgg+, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of N ∈ mathbbN distributed users, each of size L ∈ mathbbN, trained on their local data, in a privacy-preserving manner. SwiftAgg+ can significantly reduce the communication overheads without any compromise on security, and achieve optimal communication loads within diminishing gaps. Specifically, in presence of at most D=o(N) dropout users, SwiftAgg+ achieves a per-user communication load of (1+O(frac1N))L symbols and a server communication load of (1+O(frac1N))L symbols, with a worst-case information-theoretic security guarantee, against any subset of up to T=o(N) semi-honest users who may also collude with the curious server. Moreover, the proposed SwiftAgg+ allows for a flexible trade-off between communication loads and the number of active communication links. In particular, for T