# Federated Residual Learning

arXiv: https://arxiv.org/abs/2003.12880 · HTML: https://mlsanity.com/paper/2003.12880/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2020.

## Built on by (most cited first, 23 of 23)

- [Federated Learning with Partial Model Personalization](https://mlsanity.com/paper/2204.03809/) (2022)
- [Federated Multi-armed Bandits with Personalization](https://mlsanity.com/paper/2102.13101/) (2021)
- [What Do We Mean by Generalization in Federated Learning?](https://mlsanity.com/paper/2110.14216/) (2021)
- [Federated Linear Contextual Bandits](https://mlsanity.com/paper/2110.14177/) (2021)
- [Federated Multi-Armed Bandits](https://mlsanity.com/paper/2101.12204/) (2021)
- [Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective](https://mlsanity.com/paper/2111.03741/) (2021)
- [On Privacy and Personalization in Cross-Silo Federated Learning](https://mlsanity.com/paper/2206.07902/) (2022)
- [Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques](https://mlsanity.com/paper/2102.09743/) (2021)
- [Privacy and Fairness in Federated Learning: on the Perspective of Trade-off](https://mlsanity.com/paper/2306.14123/) (2023)
- [Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning](https://mlsanity.com/paper/2202.05318/) (2022)
- [On Bridging Generic and Personalized Federated Learning for Image Classification](https://mlsanity.com/paper/2107.00778/) (2021)
- [Private Multi-Task Learning: Formulation and Applications to Federated Learning](https://mlsanity.com/paper/2108.12978/) (2021)
- [Communication-Efficient Agnostic Federated Averaging](https://mlsanity.com/paper/2104.02748/) (2021)
- [Practical and Secure Federated Recommendation with Personalized Masks](https://mlsanity.com/paper/2109.02464/) (2021)
- [Federated Learning of Shareable Bases for Personalization-Friendly Image Classification](https://mlsanity.com/paper/2304.07882/) (2023)
- [Multi-Model Federated Learning with Provable Guarantees](https://mlsanity.com/paper/2207.04330/) (2022)
- [Federated Online Sparse Decision Making](https://mlsanity.com/paper/2202.13448/) (2022)
- [pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixup](https://mlsanity.com/paper/2501.11002/) (2025)
- [Federated Learning for Heterogeneous Bandits with Unobserved Contexts](https://mlsanity.com/paper/2303.17043/) (2023)
- [Harnessing the Power of Federated Learning in Federated Contextual Bandits](https://mlsanity.com/paper/2312.16341/) (2023)
- [Vertical Federated Linear Contextual Bandits](https://mlsanity.com/paper/2210.11050/) (2022)
- [Federated Offline Policy Learning](https://mlsanity.com/paper/2305.12407/) (2023)
- [Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity](https://mlsanity.com/paper/2506.00932/) (2025)

## Abstract (arXiv)

We study a new form of federated learning where the clients train personalized local models and make predictions jointly with the server-side shared model. Using this new federated learning framework, the complexity of the central shared model can be minimized while still gaining all the performance benefits that joint training provides. Our framework is robust to data heterogeneity, addressing the slow convergence problem traditional federated learning methods face when the data is non-i.i.d. across clients. We test the theory empirically and find substantial performance gains over baselines.