# A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data

arXiv: https://arxiv.org/abs/2411.07889 · HTML: https://mlsanity.com/paper/2411.07889/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Builds on (most cited first, 8 of 8)

- [Enforcing fairness in private federated learning via the modified method of differential multipliers](https://mlsanity.com/paper/2109.08604/) (2021)
- [On Privacy and Personalization in Cross-Silo Federated Learning](https://mlsanity.com/paper/2206.07902/) (2022)
- [Private Non-Convex Federated Learning Without a Trusted Server](https://mlsanity.com/paper/2203.06735/) (2022)
- [SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles](https://mlsanity.com/paper/2204.05157/) (2022)
- [Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses](https://mlsanity.com/paper/2407.09690/) (2024)
- [Analyzing Inference Privacy Risks Through Gradients in Machine Learning](https://mlsanity.com/paper/2408.16913/) (2024)
- [FedFDP: Fairness-Aware Federated Learning with Differential Privacy](https://mlsanity.com/paper/2402.16028/) (2024)
- [Exploring User-level Gradient Inversion with a Diffusion Prior](https://mlsanity.com/paper/2409.07291/) (2024)

## Abstract (arXiv)

Machine learning models are often trained on sensitive data (e.g., medical records and race/gender) that is distributed across different "silos" (e.g., hospitals). These federated learning models may then be used to make consequential decisions, such as allocating healthcare resources. Two key challenges emerge in this setting: (i) maintaining the privacy of each person's data, even if other silos or an adversary with access to the central server tries to infer this data; (ii) ensuring that decisions are fair to different demographic groups (e.g., race/gender). In this paper, we develop a novel algorithm for private and fair federated learning (FL). Our algorithm satisfies inter-silo record-level differential privacy (ISRL-DP), a strong notion of private FL requiring that silo i's sent messages satisfy record-level differential privacy for all i. Our framework can be used to promote different fairness notions, including demographic parity and equalized odds. We prove that our algorithm converges under mild smoothness assumptions on the loss function, whereas prior work required strong convexity for convergence. As a byproduct of our analysis, we obtain the first convergence guarantee for ISRL-DP nonconvex-strongly concave min-max FL. Experiments demonstrate the state-of-the-art fairness-accuracy tradeoffs of our algorithm across different privacy levels.