# Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition

arXiv: https://arxiv.org/abs/2406.19931 · HTML: https://mlsanity.com/paper/2406.19931/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 50 references that are in the map.
- First posted on arXiv 28 Jun 2024.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Ensemble Distillation for Robust Model Fusion in Federated Learning](https://mlsanity.com/paper/2006.07242/) (2020)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Think Locally, Act Globally: Federated Learning with Local and Global Representations](https://mlsanity.com/paper/2001.01523/) (2020)
- [Personalized Cross-Silo Federated Learning on Non-IID Data](https://mlsanity.com/paper/2007.03797/) (2020)
- [Personalized Federated Learning with First Order Model Optimization](https://mlsanity.com/paper/2012.08565/) (2020)
- [Federated Learning with Partial Model Personalization](https://mlsanity.com/paper/2204.03809/) (2022)
- [FedGH: Heterogeneous Federated Learning with Generalized Global Header](https://mlsanity.com/paper/2303.13137/) (2023)
- [Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing](https://mlsanity.com/paper/2007.09236/) (2020)
- [Efficient Personalized Federated Learning via Sparse Model-Adaptation](https://mlsanity.com/paper/2305.02776/) (2023)
- [Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration](https://mlsanity.com/paper/2309.11103/) (2023)
- [Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data](https://mlsanity.com/paper/2405.06413/) (2024)
- [Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients](https://mlsanity.com/paper/2304.02892/) (2023)
- [Tackling Noisy Labels with Network Parameter Additive Decomposition](https://mlsanity.com/paper/2403.13241/) (2024)
- [Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning](https://mlsanity.com/paper/2405.19789/) (2024)
- [Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space](https://mlsanity.com/paper/2307.13995/) (2023)

## Built on by (most cited first, 15 of 15)

- [Why Go Full? Elevating Federated Learning Through Partial Network Updates](https://mlsanity.com/paper/2410.11559/) (2024)
- [Beyond Higher Rank: Token-wise Input-Output Projections for Efficient Low-Rank Adaptation](https://mlsanity.com/paper/2510.23123/) (2025)
- [The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning](https://mlsanity.com/paper/2505.23176/) (2025)
- [Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation](https://mlsanity.com/paper/2407.16139/) (2024)
- [DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations](https://mlsanity.com/paper/2407.17754/) (2024)
- [Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning](https://mlsanity.com/paper/2503.13543/) (2025)
- [Choice Outweighs Effort: Facilitating Complementary Knowledge Fusion in Federated Learning via Re-calibration and Merit-discrimination](https://mlsanity.com/paper/2508.17954/) (2025)
- [FedBiF: Communication-Efficient Federated Learning via Bits Freezing](https://mlsanity.com/paper/2509.10161/) (2025)
- [Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation](https://mlsanity.com/paper/2509.20680/) (2025)
- [ML Mule: Mobile-Driven Context-Aware Collaborative Learning](https://mlsanity.com/paper/2501.07536/) (2025)
- [TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments](https://mlsanity.com/paper/2507.04327/) (2025)
- [Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning](https://mlsanity.com/paper/2507.05852/) (2025)
- [PiXTime: A Model for Federated Time Series Forecasting with Heterogeneous Data across Nodes](https://mlsanity.com/paper/2601.05613/) (2026)
- [Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting](https://mlsanity.com/paper/2603.01363/) (2026)
- [From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning](https://mlsanity.com/paper/2605.05959/) (2026)

## Abstract (arXiv)

To address data heterogeneity, the key strategy of Personalized Federated Learning (PFL) is to decouple general knowledge (shared among clients) and client-specific knowledge, as the latter can have a negative impact on collaboration if not removed. Existing PFL methods primarily adopt a parameter partitioning approach, where the parameters of a model are designated as one of two types: parameters shared with other clients to extract general knowledge and parameters retained locally to learn client-specific knowledge. However, as these two types of parameters are put together like a jigsaw puzzle into a single model during the training process, each parameter may simultaneously absorb both general and client-specific knowledge, thus struggling to separate the two types of knowledge effectively. In this paper, we introduce FedDecomp, a simple but effective PFL paradigm that employs parameter additive decomposition to address this issue. Instead of assigning each parameter of a model as either a shared or personalized one, FedDecomp decomposes each parameter into the sum of two parameters: a shared one and a personalized one, thus achieving a more thorough decoupling of shared and personalized knowledge compared to the parameter partitioning method. In addition, as we find that retaining local knowledge of specific clients requires much lower model capacity compared with general knowledge across all clients, we let the matrix containing personalized parameters be low rank during the training process. Moreover, a new alternating training strategy is proposed to further improve the performance. Experimental results across multiple datasets and varying degrees of data heterogeneity demonstrate that FedDecomp outperforms state-of-the-art methods up to 4.9%. The code is available at https://github.com/XinghaoWu/FedDecomp.