# Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning

arXiv: https://arxiv.org/abs/2309.01289 · HTML: https://mlsanity.com/paper/2309.01289/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 60 references that are in the map.
- First posted on arXiv 3 Sep 2023.

## Builds on (most cited first, 30 of 39)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Experience Replay for Continual Learning](https://mlsanity.com/paper/1811.11682/) (2018)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence](https://mlsanity.com/paper/1801.10112/) (2018)
- [Lifelong Learning with Dynamically Expandable Networks](https://mlsanity.com/paper/1708.01547/) (2017)
- [Overcoming catastrophic forgetting with hard attention to the task](https://mlsanity.com/paper/1801.01423/) (2018)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Gradient Projection Memory for Continual Learning](https://mlsanity.com/paper/2103.09762/) (2021)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [Task-Free Continual Learning](https://mlsanity.com/paper/1812.03596/) (2018)
- [Federated Continual Learning with Weighted Inter-client Transfer](https://mlsanity.com/paper/2003.03196/) (2020)
- [When the Curious Abandon Honesty: Federated Learning Is Not Private](https://mlsanity.com/paper/2112.02918/) (2021)
- [Federated Class-Incremental Learning](https://mlsanity.com/paper/2203.11473/) (2022)
- [Scalable and Order-robust Continual Learning with Additive Parameter Decomposition](https://mlsanity.com/paper/1902.09432/) (2019)
- [A continual learning survey: Defying forgetting in classification tasks](https://mlsanity.com/paper/1909.08383/) (2019)
- [Uncertainty-guided Continual Learning with Bayesian Neural Networks](https://mlsanity.com/paper/1906.02425/) (2019)
- [Generative Models for Effective ML on Private, Decentralized Datasets](https://mlsanity.com/paper/1911.06679/) (2019)
- [TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation](https://mlsanity.com/paper/2303.06937/) (2023)
- [Better Generative Replay for Continual Federated Learning](https://mlsanity.com/paper/2302.13001/) (2023)
- [Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models](https://mlsanity.com/paper/2201.12675/) (2022)
- [FLOP: Federated Learning on Medical Datasets using Partial Networks](https://mlsanity.com/paper/2102.05218/) (2021)

## Built on by (most cited first, 21 of 21)

- [Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey](https://mlsanity.com/paper/2412.13840/) (2024)
- [Personalized Federated Domain-Incremental Learning based on Adaptive Knowledge Matching](https://mlsanity.com/paper/2407.05005/) (2024)
- [Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence](https://mlsanity.com/paper/2412.13779/) (2024)
- [Federated Continual Learning for Edge-AI: A Comprehensive Survey](https://mlsanity.com/paper/2411.13740/) (2024)
- [Accurate Forgetting for Heterogeneous Federated Continual Learning](https://mlsanity.com/paper/2502.14205/) (2025)
- [SacFL: Self-Adaptive Federated Continual Learning for Resource-Constrained End Devices](https://mlsanity.com/paper/2505.00365/) (2025)
- [On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients](https://mlsanity.com/paper/2411.07959/) (2024)
- [Resource-Constrained Federated Continual Learning: What Does Matter?](https://mlsanity.com/paper/2501.08737/) (2025)
- [CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning](https://mlsanity.com/paper/2407.12188/) (2024)
- [Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?](https://mlsanity.com/paper/2411.06618/) (2024)
- [Buffer-based Gradient Projection for Continual Federated Learning](https://mlsanity.com/paper/2409.01585/) (2024)
- [FedORGP: Guiding Heterogeneous Federated Learning with Orthogonality Regularization on Global Prototypes](https://mlsanity.com/paper/2502.16119/) (2025)
- [Task-Agnostic Federated Continual Learning via Replay-Free Gradient Projection](https://mlsanity.com/paper/2509.21606/) (2025)
- [Federated Learning with New Knowledge: Fundamentals, Advances, and Futures](https://mlsanity.com/paper/2402.02268/) (2024)
- [Federated Learning of Low-Rank One-Shot Image Detection Models in Edge Devices with Scalable Accuracy and Compute Complexity](https://mlsanity.com/paper/2504.16515/) (2025)
- [FlashbackCL: Mitigating Temporal Forgetting in Federated Learning](https://mlsanity.com/paper/2606.03939/) (2026)
- [Accurate and Resource-Efficient Federated Continual Learning](https://mlsanity.com/paper/2606.11480/) (2026)
- [HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning](https://mlsanity.com/paper/2607.08784/) (2026)
- [SUM: Unified Geometric Surgery on Spatio-Temporal Adaptation Vectors for Federated Class Incremental Learning](https://mlsanity.com/paper/2607.19384/) (2026)
- [Federated Continual Learning as a Distributed Drift-Plus-Penalty Control Problem](https://mlsanity.com/paper/2608.21539/) (2026)
- [ReSCENE: Server-Side Replay for Structural Mitigation of Catastrophic Forgetting in Federated Continual Learning](https://mlsanity.com/paper/2609.38833/) (2026)

## Abstract (arXiv)

Federated Learning (FL) has gained significant attraction due to its ability to enable privacy-preserving training over decentralized data. Current literature in FL mostly focuses on single-task learning. However, over time, new tasks may appear in the clients and the global model should learn these tasks without forgetting previous tasks. This real-world scenario is known as Continual Federated Learning (CFL). The main challenge of CFL is Global Catastrophic Forgetting, which corresponds to the fact that when the global model is trained on new tasks, its performance on old tasks decreases. There have been a few recent works on CFL to propose methods that aim to address the global catastrophic forgetting problem. However, these works either have unrealistic assumptions on the availability of past data samples or violate the privacy principles of FL. We propose a novel method, Federated Orthogonal Training (FOT), to overcome these drawbacks and address the global catastrophic forgetting in CFL. Our algorithm extracts the global input subspace of each layer for old tasks and modifies the aggregated updates of new tasks such that they are orthogonal to the global principal subspace of old tasks for each layer. This decreases the interference between tasks, which is the main cause for forgetting. We empirically show that FOT outperforms state-of-the-art continual learning methods in the CFL setting, achieving an average accuracy gain of up to 15% with 27% lower forgetting while only incurring a minimal computation and communication cost.