Fundamental Limits of Information-Theoretic Secure Aggregation with User Dropouts in Federated Learning

Key points

• With user dropouts, correlated random keys protect local gradients while allowing recovery of the desired gradient sum.1
• Existing information-theoretic schemes typically design mask-elimination communication for a prescribed dropout threshold and evaluate its cost under worst-case dropouts across the masked-gradient upload and mask-elimination phases.1
• To address these limitations, we formulate an adaptive secure aggregation framework under the T-colluding security constraint and construct a single scheme that supports all feasible dropout scenarios by adapting its communication to the actual number of dropouts.1
• These results characterize the fundamental randomness and communication costs of secure aggregation with user dropouts.1

Sources (1)

• [1]Fundamental Limits of Information-Theoretic Secure Aggregation with User Dropouts in Federated Learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 03:49 AM

  Secure aggregation enables a server to compute the sum of users' gradients or model updates without revealing individual values.

  With user dropouts, correlated random keys protect local gradients while allowing recovery of the desired gradient sum.

Extractive summary: sentences quoted from the sources.