mlsanityData to 14 Oct 2026

Information Theoretic Secure Aggregation with User Dropouts

arXiv 2101.07750 ↗First posted 19 Jan 2021cs.IT cs.CR cs.LGcited by 19

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Abstract (arXiv)

In the robust secure aggregation problem, a server wishes to learn and only learn the sum of the inputs of a number of users while some users may drop out (i.e., may not respond). The identity of the dropped users is not known a priori and the server needs to securely recover the sum of the remaining surviving users. We consider the following minimal two-round model of secure aggregation. Over the first round, any set of no fewer than U users out of K users respond to the server and the server wants to learn the sum of the inputs of all responding users. The remaining users are viewed as dropped. Over the second round, any set of no fewer than U users of the surviving users respond (i.e., dropouts are still possible over the second round) and from the information obtained from the surviving users over the two rounds, the server can decode the desired sum. The security constraint is that even if the server colludes with any T users and the messages from the dropped users are received by the server (e.g., delayed packets), the server is not able to infer any additional information beyond the sum in the information theoretic sense. For this information theoretic secure aggregation problem, we characterize the optimal communication cost. When U ≤ T, secure aggregation is not feasible, and when U > T, to securely compute one symbol of the sum, the minimum number of symbols sent from each user to the server is 1 over the first round, and 1/(U-T) over the second round.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 19 of its 52 references that are in the map. First posted on arXiv 19 Jan 2021.

Builds on

19 of 52 references.

References · 19

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  4. 2019Robust Aggregation for Federated Learningcited by 317
  5. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  6. 2020Byzantine-Resilient Secure Federated Learningcited by 93
  7. 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78
  8. 2020FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learningcited by 61
  9. 2019HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learningcited by 51
  10. 2019Federated Learning with Autotuned Communication-Efficient Secure Aggregationcited by 23
  11. 2020Secure Aggregation with Heterogeneous Quantization in Federated Learningcited by 21
  12. 2020Communication-Computation Efficient Secure Aggregation for Federated Learningcited by 18
7 more
  1. 2020Privacy Preservation in Federated Learning: An insightful survey from the GDPR Perspectivecited by 7
  2. 2020Efficient Sparse Secure Aggregation for Federated Learningcited by 6
  3. 2016The Capacity of Symmetric Private Information Retrievalcited by 5
  4. 2020Private Weighted Sum Aggregationcited by 4
  5. 2015Communication and Randomness Lower Bounds for Secure Computationcited by 1
  6. 2019On the Information Leakage in Private Information Retrieval Systemscited by 1
  7. 2020Expand-and-Randomize: An Algebraic Approach to Secure Computationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 2; 2023: 4; 2024: 3; 2025: 3; 2026: 2.

19 papers.

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  1. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 64
  2. 2021Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learningcited by 39
  3. 2023LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulationcited by 36
  4. 2021CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learningcited by 20
  5. 2022How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?cited by 19
  6. 2022SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learningcited by 18
  7. 2023Efficient and Secure Federated Learning for Financial Applicationscited by 17
  8. 2024The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscapecited by 13
  9. 2021Secure Aggregation for Buffered Asynchronous Federated Learningcited by 10
  10. 2021Sparsified Secure Aggregation for Privacy-Preserving Federated Learningcited by 8
  11. 2025Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Associationcited by 7
  12. 2023Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusioncited by 5
7 more
  1. 2023ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitmentcited by 4
  2. 2024Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimationcited by 4
  3. 2025Hierarchical Secure Aggregation with Heterogeneous Security Constraints and Arbitrary User Collusioncited by 2
  4. 2025Information-Theoretic Decentralized Secure Aggregation with Passive Collusion Resiliencecited by 2
  5. 2024Differentially Private Federated Learning without Noise Addition: When is it Possible?cited by 0
  6. 2026The Capacity of Information-Theoretic Secure Aggregation in Federated Learningcited by 0
  7. 2026Secure Aggregation with Top-K Sparsification in Decentralized Federated Learningcited by 0