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Secure Embedding Aggregation for Federated Representation Learning

arXiv 2206.09097 ↗First posted 18 Jun 2022cs.LG cs.CR cs.ITcited by 0

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Abstract (arXiv)

We consider a federated representation learning framework, where with the assistance of a central server, a group of N distributed clients train collaboratively over their private data, for the representations (or embeddings) of a set of entities (e.g., users in a social network). Under this framework, for the key step of aggregating local embeddings trained privately at the clients, we develop a secure embedding aggregation protocol named scheme, which leverages all potential aggregation opportunities among all the clients, while providing privacy guarantees for the set of local entities and corresponding embeddings simultaneously at each client, against a curious server and up to T < N/2 colluding clients.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 37 references that are in the map. First posted on arXiv 18 Jun 2022.

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18 of 37 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  3. 2019Federated Machine Learning: Concept and Applicationscited by 412
  4. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  5. 2019Secure Federated Matrix Factorizationcited by 102
  6. 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78
  7. 2018Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacycited by 66
  8. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 64
  9. 2020FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learningcited by 61
  10. 2020FedE: Embedding Knowledge Graphs in Federated Settingcited by 46
  11. 2022SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learningcited by 18
  12. 2022Exploiting Data Sparsity in Secure Cross-Platform Social Recommendationcited by 17
6 more
  1. 2022SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guaranteescited by 14
  2. 2020Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learningcited by 12
  3. 2021Private Retrieval, Computing and Learning: Recent Progress and Future Challengescited by 12
  4. 2021Private Hierarchical Clustering in Federated Networkscited by 8
  5. 2016The Capacity of Private Information Retrievalcited by 6
  6. 2016The Capacity of Private Information Retrieval from Coded Databasescited by 6

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