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

arXiv: https://arxiv.org/abs/2406.03766 · HTML: https://mlsanity.com/paper/2406.03766/

## 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 (most cited first, 30 of 34)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [The Composition Theorem for Differential Privacy](https://mlsanity.com/paper/1311.0776/) (2013)
- [Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences](https://mlsanity.com/paper/1807.01647/) (2018)
- [Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)](https://mlsanity.com/paper/1812.11494/) (2018)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [Distributed Mean Estimation with Limited Communication](https://mlsanity.com/paper/1611.00429/) (2016)
- [Fast Federated Learning in the Presence of Arbitrary Device Unavailability](https://mlsanity.com/paper/2106.04159/) (2021)
- [Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling](https://mlsanity.com/paper/2112.11256/) (2021)
- [Towards Flexible Device Participation in Federated Learning](https://mlsanity.com/paper/2006.06954/) (2020)
- [A Unified Analysis of Federated Learning with Arbitrary Client Participation](https://mlsanity.com/paper/2205.13648/) (2022)
- [Anarchic Federated Learning](https://mlsanity.com/paper/2108.09875/) (2021)
- [Privacy Amplification via Random Check-Ins](https://mlsanity.com/paper/2007.06605/) (2020)
- [Optimal Algorithms for Mean Estimation under Local Differential Privacy](https://mlsanity.com/paper/2205.02466/) (2022)
- [Millimeter Wave Channel Modeling and Cellular Capacity Evaluation](https://mlsanity.com/paper/1312.4921/) (2013)
- [Federated Learning under Importance Sampling](https://mlsanity.com/paper/2012.07383/) (2020)
- [Distributed k-Means and k-Median Clustering on General Topologies](https://mlsanity.com/paper/1306.0604/) (2013)
- [Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation](https://mlsanity.com/paper/2103.01953/) (2021)
- [Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability](https://mlsanity.com/paper/2002.07399/) (2020)
- [Decentralized SGD with Over-the-Air Computation](https://mlsanity.com/paper/2003.04216/) (2020)
- [Private Wireless Federated Learning with Anonymous Over-the-Air Computation](https://mlsanity.com/paper/2011.08579/) (2020)
- [Semi-Decentralized Federated Learning with Collaborative Relaying](https://mlsanity.com/paper/2205.10998/) (2022)
- [Locally Private Mean Estimation: Z-test and Tight Confidence Intervals](https://mlsanity.com/paper/1810.08054/) (2018)
- [MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates](https://mlsanity.com/paper/2306.12212/) (2023)
- [Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation](https://mlsanity.com/paper/2306.04924/) (2023)
- [Mean Estimation with User-level Privacy under Data Heterogeneity](https://mlsanity.com/paper/2307.15835/) (2023)
- [Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation](https://mlsanity.com/paper/2110.07751/) (2021)

## Built on by (most cited first, 2 of 2)

- [Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning](https://mlsanity.com/paper/2505.11304/) (2025)
- [Coding-Enforced Resilient and Secure Aggregation for Hierarchical Federated Learning](https://mlsanity.com/paper/2601.17995/) (2026)

## 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.