Buffer-based Gradient Projection for Continual Federated Learning

• URL: http://arxiv.org/abs/2409.01585v1
• Date: Tue, 3 Sep 2024 03:50:19 GMT
• Title: Buffer-based Gradient Projection for Continual Federated Learning
• Authors: Shenghong Dai, Jy-yong Sohn, Yicong Chen, S M Iftekharul Alam, Ravikumar Balakrishnan, Suman Banerjee, Nageen Himayat, Kangwook Lee,
• Abstract summary: Fed-A-GEM mitigates catastrophic forgetting by leveraging local buffer samples and aggregated buffer gradients. Our experiments on standard benchmarks show consistent performance improvements across diverse scenarios.
• Score: 16.879024856283323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continual Federated Learning (CFL) is essential for enabling real-world applications where multiple decentralized clients adaptively learn from continuous data streams. A significant challenge in CFL is mitigating catastrophic forgetting, where models lose previously acquired knowledge when learning new information. Existing approaches often face difficulties due to the constraints of device storage capacities and the heterogeneous nature of data distributions among clients. While some CFL algorithms have addressed these challenges, they frequently rely on unrealistic assumptions about the availability of task boundaries (i.e., knowing when new tasks begin). To address these limitations, we introduce Fed-A-GEM, a federated adaptation of the A-GEM method (Chaudhry et al., 2019), which employs a buffer-based gradient projection approach. Fed-A-GEM alleviates catastrophic forgetting by leveraging local buffer samples and aggregated buffer gradients, thus preserving knowledge across multiple clients. Our method is combined with existing CFL techniques, enhancing their performance in the CFL context. Our experiments on standard benchmarks show consistent performance improvements across diverse scenarios. For example, in a task-incremental learning scenario using the CIFAR-100 dataset, our method can increase the accuracy by up to 27%. Our code is available at https://github.com/shenghongdai/Fed-A-GEM.

Related papers

• On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients [2.2530496464901106]
  The holy grail of machine learning is to enable Continual Federated Learning (CFL) to enhance the efficiency, privacy, and scalability of AI systems while learning from streaming data. We propose a novel replay-memory based federated strategy consisting of edge-based gradient updates on memory and aggregated gradients on the current data. We empirically show that C-FLAG outperforms several state-of-the-art baselines on both task and class-incremental settings with respect to metrics such as accuracy and forgetting.
  arXiv  Detail & Related papers  (2024-11-12T17:36:20Z)
• Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning [13.836798036474143]
  Key challenge in Federated Class Continual Learning is catastrophic forgetting. We propose a novel method of data replay based on diffusion models. Our method significantly outperforms existing baselines.
  arXiv  Detail & Related papers  (2024-09-02T10:07:24Z)
• Flashback: Understanding and Mitigating Forgetting in Federated Learning [7.248285042377168]
  In Federated Learning (FL), forgetting, or the loss of knowledge across rounds, hampers algorithm convergence. We introduce a metric to measure forgetting granularly, ensuring distinct recognition amid new knowledge acquisition. We propose Flashback, an FL algorithm with a dynamic distillation approach that is used to regularize the local models, and effectively aggregate their knowledge.
  arXiv  Detail & Related papers  (2024-02-08T10:52:37Z)
• Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning [55.49099125128281]
  We propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation. We show that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.
  arXiv  Detail & Related papers  (2023-11-03T13:34:44Z)
• Towards Instance-adaptive Inference for Federated Learning [80.38701896056828]
  Federated learning (FL) is a distributed learning paradigm that enables multiple clients to learn a powerful global model by aggregating local training. In this paper, we present a novel FL algorithm, i.e., FedIns, to handle intra-client data heterogeneity by enabling instance-adaptive inference in the FL framework. Our experiments show that our FedIns outperforms state-of-the-art FL algorithms, e.g., a 6.64% improvement against the top-performing method with less than 15% communication cost on Tiny-ImageNet.
  arXiv  Detail & Related papers  (2023-08-11T09:58:47Z)
• Improving information retention in large scale online continual learning [99.73847522194549]
  Online continual learning aims to adapt efficiently to new data while retaining existing knowledge. Recent work suggests that information retention remains a problem in large scale OCL even when the replay buffer is unlimited. We propose using a moving average family of methods to improve optimization for non-stationary objectives.
  arXiv  Detail & Related papers  (2022-10-12T16:59:43Z)
• Acceleration of Federated Learning with Alleviated Forgetting in Local Training [61.231021417674235]
  Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy. We propose FedReg, an algorithm to accelerate FL with alleviated knowledge forgetting in the local training stage. Our experiments demonstrate that FedReg not only significantly improves the convergence rate of FL, especially when the neural network architecture is deep.
  arXiv  Detail & Related papers  (2022-03-05T02:31:32Z)
• Towards Federated Learning on Time-Evolving Heterogeneous Data [13.080665001587281]
  Federated Learning (FL) is an emerging learning paradigm that preserves privacy by ensuring client data locality on edge devices. Despite recent research efforts on improving the optimization of heterogeneous data, the impact of time-evolving heterogeneous data in real-world scenarios has not been well studied. We propose Continual Federated Learning (CFL), a flexible framework, to capture the time-evolving heterogeneity of FL.
  arXiv  Detail & Related papers  (2021-12-25T14:58:52Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)
• Bayesian Federated Learning over Wireless Networks [87.37301441859925]
  Federated learning is a privacy-preserving and distributed training method using heterogeneous data sets stored at local devices. This paper presents an efficient modified BFL algorithm called scalableBFL (SBFL)
  arXiv  Detail & Related papers  (2020-12-31T07:32:44Z)

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.