Catastrophic Forgetting Resilient One-Shot Incremental Federated Learning

• URL: http://arxiv.org/abs/2602.17625v1
• Date: Thu, 19 Feb 2026 18:44:23 GMT
• Title: Catastrophic Forgetting Resilient One-Shot Incremental Federated Learning
• Authors: Obaidullah Zaland, Zulfiqar Ahmad Khan, Monowar Bhuyan,
• Abstract summary: This paper presents One-Shot Incremental Federated Learning (OSI-FL), the first FL framework that addresses the dual challenges of communication overhead and catastrophic forgetting.<n>OSI-FL communicates category-specific embeddings, devised by a frozen vision-language model (VLM) from each client in a single communication round.<n>We augment training with Selective Sample Retention (SSR), which identifies and retains the top-p most informative samples per category and task pair.
• Score: 3.4263731151809593
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern big-data systems generate massive, heterogeneous, and geographically dispersed streams that are large-scale and privacy-sensitive, making centralization challenging. While federated learning (FL) provides a privacy-enhancing training mechanism, it assumes a static data flow and learns a collaborative model over multiple rounds, making learning with \textit{incremental} data challenging in limited-communication scenarios. This paper presents One-Shot Incremental Federated Learning (OSI-FL), the first FL framework that addresses the dual challenges of communication overhead and catastrophic forgetting. OSI-FL communicates category-specific embeddings, devised by a frozen vision-language model (VLM) from each client in a single communication round, which a pre-trained diffusion model at the server uses to synthesize new data similar to the client's data distribution. The synthesized samples are used on the server for training. However, two challenges still persist: i) tasks arriving incrementally need to retrain the global model, and ii) as future tasks arrive, retraining the model introduces catastrophic forgetting. To this end, we augment training with Selective Sample Retention (SSR), which identifies and retains the top-p most informative samples per category and task pair based on sample loss. SSR bounds forgetting by ensuring that representative retained samples are incorporated into training in further iterations. The experimental results indicate that OSI-FL outperforms baselines, including traditional and one-shot FL approaches, in both class-incremental and domain-incremental scenarios across three benchmark datasets.

Related papers

• Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data [19.33095080645165]
  Fed-REACT is a federated learning framework designed for heterogeneous and evolving client data.<n>We provide a theoretical analysis of the representation learning stage, and empirically demonstrate that Fed-REACT achieves superior accuracy and robustness on real-world datasets.
  arXiv  Detail & Related papers  (2025-09-08T20:24:40Z)
• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality [41.79433449873368]
  We propose a novel multi-modal federated learning method, Federated Multi-modal contrastiVe training with Pre-trained completion (FedMVP) FedMVP integrates the large-scale pre-trained models to enhance the federated training. We demonstrate that the model achieves superior performance over two real-world image-text classification datasets.
  arXiv  Detail & Related papers  (2024-06-16T19:18:06Z)
• Decoupled Federated Learning on Long-Tailed and Non-IID data with Feature Statistics [20.781607752797445]
  We propose a two-stage Decoupled Federated learning framework using Feature Statistics (DFL-FS) In the first stage, the server estimates the client's class coverage distributions through masked local feature statistics clustering. In the second stage, DFL-FS employs federated feature regeneration based on global feature statistics to enhance the model's adaptability to long-tailed data distributions.
  arXiv  Detail & Related papers  (2024-03-13T09:24:59Z)
• Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization [165.98557106089777]
  A key challenge is to enhance the capabilities of large language models (LLMs) amid a looming shortage of high-quality training data. Our study starts from an empirical strategy for the light continual training of LLMs using their original pre-training data sets. We then formalize this strategy into a principled framework of Instance-Reweighted Distributionally Robust Optimization.
  arXiv  Detail & Related papers  (2024-02-22T04:10:57Z)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarization [36.916155654985936]
  Meeting summarization has emerged as a promising technique for providing users with condensed summaries. We propose adapter-based Federated Selective Knowledge Distillation (AdaFedSelecKD) for training performant client models.
  arXiv  Detail & Related papers  (2023-08-07T03:34:01Z)
• Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity [72.85248553787538]
  This paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. We formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE) Experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
  arXiv  Detail & Related papers  (2023-08-04T04:18:01Z)
• Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model [40.83058938096914]
  We propose FedDISC, a Federated Diffusion-Inspired Semi-supervised Co-training method. We first extract prototypes of the labeled server data and use these prototypes to predict pseudo-labels of the client data. For each category, we compute the cluster centroids and domain-specific representations to signify the semantic and stylistic information of their distributions. These representations are sent back to the server, which uses the pre-trained to generate synthetic datasets complying with the client distributions and train a global model on it.
  arXiv  Detail & Related papers  (2023-05-06T14:22:33Z)
• Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning [60.41501515192088]
  Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively. The data samples usually follow a long-tailed distribution in the real world, and FL on the decentralized and long-tailed data yields a poorly-behaved global model. In this work, we integrate the local real data with the global gradient prototypes to form the local balanced datasets.
  arXiv  Detail & Related papers  (2023-01-25T03:18:10Z)
• FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning [87.08902493524556]
  Federated learning(FL) has recently attracted increasing attention from academia and industry. We propose FedDM to build the global training objective from multiple local surrogate functions. In detail, we construct synthetic sets of data on each client to locally match the loss landscape from original data.
  arXiv  Detail & Related papers  (2022-07-20T04:55:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.