mlsanityData to 14 Oct 2026

Privacy Preserving Semi-Decentralized Mean Estimation over Intermittently-Connected Networks

arXiv 2406.03766 ↗First posted 6 Jun 2024eess.SP cs.DC cs.ITcited by 2

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Abstract (arXiv)

We consider the problem of privately estimating the mean of vectors distributed across different nodes of an unreliable wireless network, where communications between nodes can fail intermittently. We adopt a semi-decentralized setup, wherein to mitigate the impact of intermittently connected links, nodes can collaborate with their neighbors to compute a local consensus, which they relay to a central server. In such a setting, the communications between any pair of nodes must ensure that the privacy of the nodes is rigorously maintained to prevent unauthorized information leakage. We study the tradeoff between collaborative relaying and privacy leakage due to the data sharing among nodes and, subsequently, propose PriCER: Private Collaborative Estimation via Relaying -- a differentially private collaborative algorithm for mean estimation to optimize this tradeoff. The privacy guarantees of PriCER arise (i) implicitly, by exploiting the inherent stochasticity of the flaky network connections, and (ii) explicitly, by adding Gaussian perturbations to the estimates exchanged by the nodes. Local and central privacy guarantees are provided against eavesdroppers who can observe different signals, such as the communications amongst nodes during local consensus and (possibly multiple) transmissions from the relays to the central server. We substantiate our theoretical findings with numerical simulations. Our implementation is available at https://github.com/rajarshisaha95/private-collaborative-relaying.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 34 of its 51 references that are in the map. First posted on arXiv 6 Jun 2024.

Builds on

34 of 51 references.

References · 34

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22 more
  1. 2020Towards Flexible Device Participation in Federated Learningcited by 65
  2. 2022A Unified Analysis of Federated Learning with Arbitrary Client Participationcited by 45
  3. 2021Anarchic Federated Learningcited by 42
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learningcited by 1
  2. 2026Coding-Enforced Resilient and Secure Aggregation for Hierarchical Federated Learningcited by 0