Perfectly-Private Analog Secure Aggregation in Federated Learning

• URL: http://arxiv.org/abs/2509.08683v2
• Date: Wed, 17 Sep 2025 09:59:29 GMT
• Title: Perfectly-Private Analog Secure Aggregation in Federated Learning
• Authors: Delio Jaramillo-Velez, Charul Rajput, Ragnar Freij-Hollanti, Camilla Hollanti, Alexandre Graell i Amat,
• Abstract summary: In federated learning, multiple parties train models locally and share their parameters with a central server, which aggregates them to update a global model.<n>In this paper, a novel secure parameter aggregation method is proposed that employs the torus rather than a finite field.
• Score: 51.61616734974475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In federated learning, multiple parties train models locally and share their parameters with a central server, which aggregates them to update a global model. To address the risk of exposing sensitive data through local models, secure aggregation via secure multiparty computation has been proposed to enhance privacy. At the same time, perfect privacy can only be achieved by a uniform distribution of the masked local models to be aggregated. This raises a problem when working with real valued data, as there is no measure on the reals that is invariant under the masking operation, and hence information leakage is bound to occur. Shifting the data to a finite field circumvents this problem, but as a downside runs into an inherent accuracy complexity tradeoff issue due to fixed point modular arithmetic as opposed to floating point numbers that can simultaneously handle numbers of varying magnitudes. In this paper, a novel secure parameter aggregation method is proposed that employs the torus rather than a finite field. This approach guarantees perfect privacy for each party's data by utilizing the uniform distribution on the torus, while avoiding accuracy losses. Experimental results show that the new protocol performs similarly to the model without secure aggregation while maintaining perfect privacy. Compared to the finite field secure aggregation, the torus-based protocol can in some cases significantly outperform it in terms of model accuracy and cosine similarity, hence making it a safer choice.

Related papers

• Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response [1.9949261242626626]
  In machine learning, each client trains a local model using its own data. The trained models are periodically transmitted to the central server.<n>The server then combines the received models using federated aggregation algorithms to obtain a global model.<n>This global model is distributed back to the clients, and the process continues in a cyclical manner.<n>Attackers can perform inference attacks on the obtained models to approximate the training dataset, potentially causing data leakage.
  arXiv  Detail & Related papers  (2025-09-17T13:59:38Z)
• PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning [49.916365792036636]
  Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data.<n>The transmitted model updates can potentially leak sensitive user information, and the lack of central control of the local training process leaves the global model susceptible to malicious manipulations on model updates.<n>We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy.
  arXiv  Detail & Related papers  (2024-07-12T03:18:08Z)
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• Enabling Privacy-preserving Model Evaluation in Federated Learning via Fully Homomorphic Encryption [1.9662978733004604]
  Federated learning has become increasingly widespread due to its ability to train models collaboratively without centralizing sensitive data.<n>The evaluation phase presents significant privacy risks that have not been adequately addressed in the literature.<n>We propose a novel evaluation method that leverages fully homomorphic encryption.
  arXiv  Detail & Related papers  (2024-03-21T14:36:55Z)
• Client-specific Property Inference against Secure Aggregation in Federated Learning [52.8564467292226]
  Federated learning has become a widely used paradigm for collaboratively training a common model among different participants. Many attacks have shown that it is still possible to infer sensitive information such as membership, property, or outright reconstruction of participant data. We show that simple linear models can effectively capture client-specific properties only from the aggregated model updates.
  arXiv  Detail & Related papers  (2023-03-07T14:11:01Z)
• FedCC: Robust Federated Learning against Model Poisoning Attacks [0.0]
  Federated learning is a distributed framework designed to address privacy concerns.<n>It introduces new attack surfaces, which are especially prone when data is non-Independently and Identically Distributed.<n>We present FedCC, a simple yet effective novel defense algorithm against model poisoning attacks.
  arXiv  Detail & Related papers  (2022-12-05T01:52:32Z)
• Differentially private partitioned variational inference [28.96767727430277]
  Learning a privacy-preserving model from sensitive data which are distributed across multiple devices is an increasingly important problem. We present differentially private partitioned variational inference, the first general framework for learning a variational approximation to a Bayesian posterior distribution.
  arXiv  Detail & Related papers  (2022-09-23T13:58:40Z)
• Suppressing Poisoning Attacks on Federated Learning for Medical Imaging [4.433842217026879]
  We propose a robust aggregation rule called Distance-based Outlier Suppression (DOS) that is resilient to byzantine failures. The proposed method computes the distance between local parameter updates of different clients and obtains an outlier score for each client. The resulting outlier scores are converted into normalized weights using a softmax function, and a weighted average of the local parameters is used for updating the global model.
  arXiv  Detail & Related papers  (2022-07-15T00:43:34Z)
• BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning [0.0]
  We present BEAS, the first blockchain-based framework for N-party Federated Learning. It provides strict privacy guarantees of training data using gradient pruning. Anomaly detection protocols are used to minimize the risk of data-poisoning attacks. We also define a novel protocol to prevent premature convergence in heterogeneous learning environments.
  arXiv  Detail & Related papers  (2022-02-06T17:11:14Z)
• Secure PAC Bayesian Regression via Real Shamir Secret Sharing [2.578242050187029]
  We present a protocol for learning a linear model relying on recently described technique called real number secret sharing. We consider the situation where several parties hold different data instances and they are not willing to give up the privacy of the data. We suggest two methods; a secure inverse method and a secure Gaussian elimination method, and compare these methods at the end.
  arXiv  Detail & Related papers  (2021-09-23T08:15:22Z)
• Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge Computing [113.52575069030192]
  Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones and vehicles. Due to the limitations of communication costs and security requirements, it is of paramount importance to extract information in a decentralized manner instead of aggregating data to a fusion center. We consider the problem of learning model parameters in a multi-agent system with data locally processed via distributed edge nodes. A class of mini-batch alternating direction method of multipliers (ADMM) algorithms is explored to develop the distributed learning model.
  arXiv  Detail & Related papers  (2020-10-02T10:41:59Z)
• Differentially Private Federated Learning with Laplacian Smoothing [72.85272874099644]
  Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users. An adversary may still be able to infer the private training data by attacking the released model. Differential privacy provides a statistical protection against such attacks at the price of significantly degrading the accuracy or utility of the trained models.
  arXiv  Detail & Related papers  (2020-05-01T04:28:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.