# Federated One-Shot Learning with Data Privacy and Objective-Hiding

arXiv: https://arxiv.org/abs/2504.21182 · HTML: https://mlsanity.com/paper/2504.21182/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 80 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 30 of 30)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Ensemble Distillation for Robust Model Fusion in Federated Learning](https://mlsanity.com/paper/2006.07242/) (2020)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning](https://mlsanity.com/paper/2002.04156/) (2020)
- [Adaptive Multi-Teacher Multi-level Knowledge Distillation](https://mlsanity.com/paper/2103.04062/) (2021)
- [LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2109.14236/) (2021)
- [Unifying Heterogeneous Classifiers with Distillation](https://mlsanity.com/paper/1904.06062/) (2019)
- [CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2112.08909/) (2021)
- [SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning](https://mlsanity.com/paper/2203.13060/) (2022)
- [Federated Distillation: A Survey](https://mlsanity.com/paper/2404.08564/) (2024)
- [Private Retrieval, Computing and Learning: Recent Progress and Future Challenges](https://mlsanity.com/paper/2108.00026/) (2021)
- [Auditing Private Prediction](https://mlsanity.com/paper/2402.09403/) (2024)
- [Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusion](https://mlsanity.com/paper/2306.14088/) (2023)
- [Private Function Retrieval](https://mlsanity.com/paper/1711.04677/) (2017)
- [Semantic Private Information Retrieval](https://mlsanity.com/paper/2003.13667/) (2020)
- [Private Polynomial Computation from Lagrange Encoding](https://mlsanity.com/paper/1812.04142/) (2018)
- [Reducing the gap between streaming and non-streaming Transducer-based ASR by adaptive two-stage knowledge distillation](https://mlsanity.com/paper/2306.15171/) (2023)
- [Private Information Retrieval in Graph Based Replication Systems](https://mlsanity.com/paper/1812.01566/) (2018)
- [Private Read Update Write (PRUW) in Federated Submodel Learning (FSL): Communication Efficient Schemes With and Without Sparsification](https://mlsanity.com/paper/2209.04421/) (2022)
- [Private Information Retrieval from Non-Replicated Databases](https://mlsanity.com/paper/1901.00004/) (2018)
- [Symmetric Private Information Retrieval with User-Side Common Randomness](https://mlsanity.com/paper/2105.05807/) (2021)
- [Single-Server Private Linear Transformation: The Joint Privacy Case](https://mlsanity.com/paper/2106.05220/) (2021)
- [Multi-Server Private Linear Computation with Joint and Individual Privacy Guarantees](https://mlsanity.com/paper/2108.09271/) (2021)
- [Multi-Server Private Linear Transformation with Joint Privacy](https://mlsanity.com/paper/2108.09843/) (2021)
- [Digital Blind Box: Random Symmetric Private Information Retrieval](https://mlsanity.com/paper/2205.07828/) (2022)
- [Privacy-Preserving Polynomial Computing Over Distributed Data](https://mlsanity.com/paper/2309.09315/) (2023)

## Abstract (arXiv)

Privacy in federated learning is crucial, encompassing two key aspects: safeguarding the privacy of clients' data and maintaining the privacy of the federator's objective from the clients. While the first aspect has been extensively studied, the second has received much less attention. We present a novel approach that addresses both concerns simultaneously, drawing inspiration from techniques in knowledge distillation and private information retrieval to provide strong information-theoretic privacy guarantees. Traditional private function computation methods could be used here; however, they are typically limited to linear or polynomial functions. To overcome these constraints, our approach unfolds in three stages. In stage 0, clients perform the necessary computations locally. In stage 1, these results are shared among the clients, and in stage 2, the federator retrieves its desired objective without compromising the privacy of the clients' data. The crux of the method is a carefully designed protocol that combines secret-sharing-based multi-party computation and a graph-based private information retrieval scheme. We show that our method outperforms existing tools from the literature when properly adapted to this setting.