# Provably Personalized and Robust Federated Learning

arXiv: https://arxiv.org/abs/2306.08393 · HTML: https://mlsanity.com/paper/2306.08393/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 33 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 24 of 24)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Taskonomy: Disentangling Task Transfer Learning](https://mlsanity.com/paper/1804.08328/) (2018)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [The Hidden Vulnerability of Distributed Learning in Byzantium](https://mlsanity.com/paper/1802.07927/) (2018)
- [Three Approaches for Personalization with Applications to Federated Learning](https://mlsanity.com/paper/2002.10619/) (2020)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Lower Bounds for Non-Convex Stochastic Optimization](https://mlsanity.com/paper/1912.02365/) (2019)
- [A Little Is Enough: Circumventing Defenses For Distributed Learning](https://mlsanity.com/paper/1902.06156/) (2019)
- [Federated Multi-Task Learning under a Mixture of Distributions](https://mlsanity.com/paper/2108.10252/) (2021)
- [Survey of Personalization Techniques for Federated Learning](https://mlsanity.com/paper/2003.08673/) (2020)
- [Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron](https://mlsanity.com/paper/1810.07288/) (2018)
- [Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation](https://mlsanity.com/paper/1903.03936/) (2019)
- [Learning from History for Byzantine Robust Optimization](https://mlsanity.com/paper/2012.10333/) (2020)
- [Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing](https://mlsanity.com/paper/2006.09365/) (2020)
- [Personalized Federated Learning through Local Memorization](https://mlsanity.com/paper/2111.09360/) (2021)
- [Optimality and Stability in Federated Learning: A Game-theoretic Approach](https://mlsanity.com/paper/2106.09580/) (2021)
- [Personalized Federated Learning under Mixture of Distributions](https://mlsanity.com/paper/2305.01068/) (2023)
- [Personalized Federated Learning via Heterogeneous Modular Networks](https://mlsanity.com/paper/2210.14830/) (2022)

## Built on by (most cited first, 11 of 11)

- [Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning](https://mlsanity.com/paper/2406.03519/) (2024)
- [CoBo: Collaborative Learning via Bilevel Optimization](https://mlsanity.com/paper/2409.05539/) (2024)
- [Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients](https://mlsanity.com/paper/2409.20329/) (2024)
- [Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare](https://mlsanity.com/paper/2410.07039/) (2024)
- [Adaptive collaboration for online personalized distributed learning with heterogeneous clients](https://mlsanity.com/paper/2507.06844/) (2025)
- [Collaborative and Efficient Personalization with Mixtures of Adaptors](https://mlsanity.com/paper/2410.03497/) (2024)
- [CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings](https://mlsanity.com/paper/2506.22427/) (2025)
- [Defection-Free Collaboration between Competitors in a Learning System](https://mlsanity.com/paper/2406.15898/) (2024)
- [Fast convergence of a Federated Expectation-Maximization Algorithm](https://mlsanity.com/paper/2408.05819/) (2024)
- [Your Data, My Model: Learning Who Really Helps in Federated Learning](https://mlsanity.com/paper/2409.02064/) (2024)
- [SP-CACW: Convergence-Aware Client Weighting for Selfish Personalized Learning](https://mlsanity.com/paper/2606.29322/) (2026)

## Abstract (arXiv)

Identifying clients with similar objectives and learning a model-per-cluster is an intuitive and interpretable approach to personalization in federated learning. However, doing so with provable and optimal guarantees has remained an open challenge. We formalize this problem as a stochastic optimization problem, achieving optimal convergence rates for a large class of loss functions. We propose simple iterative algorithms which identify clusters of similar clients and train a personalized model-per-cluster, using local client gradients and flexible constraints on the clusters. The convergence rates of our algorithms asymptotically match those obtained if we knew the true underlying clustering of the clients and are provably robust in the Byzantine setting where some fraction of the clients are malicious.