Federated Continual 3D Segmentation With Single-round Communication

• URL: http://arxiv.org/abs/2503.15414v1
• Date: Wed, 19 Mar 2025 16:56:34 GMT
• Title: Federated Continual 3D Segmentation With Single-round Communication
• Authors: Can Peng, Qianhui Men, Pramit Saha, Qianye Yang, Cheng Ouyang, J. Alison Noble,
• Abstract summary: Federated learning seeks to foster collaboration among distributed clients while preserving the privacy of their local data.<n>In such a dynamic federated analysis setup, the conventional federated communication strategy of model aggregation per communication round is suboptimal.<n>In this paper, we propose a federated continual learning strategy that employs a one-time model aggregation at the server through multi-model distillation.
• Score: 8.95113819926435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning seeks to foster collaboration among distributed clients while preserving the privacy of their local data. Traditionally, federated learning methods assume a fixed setting in which client data and learning objectives remain constant. However, in real-world scenarios, new clients may join, and existing clients may expand the segmentation label set as task requirements evolve. In such a dynamic federated analysis setup, the conventional federated communication strategy of model aggregation per communication round is suboptimal. As new clients join, this strategy requires retraining, linearly increasing communication and computation overhead. It also imposes requirements for synchronized communication, which is difficult to achieve among distributed clients. In this paper, we propose a federated continual learning strategy that employs a one-time model aggregation at the server through multi-model distillation. This approach builds and updates the global model while eliminating the need for frequent server communication. When integrating new data streams or onboarding new clients, this approach efficiently reuses previous client models, avoiding the need to retrain the global model across the entire federation. By minimizing communication load and bypassing the need to put unchanged clients online, our approach relaxes synchronization requirements among clients, providing an efficient and scalable federated analysis framework suited for real-world applications. Using multi-class 3D abdominal CT segmentation as an application task, we demonstrate the effectiveness of the proposed approach.

Related papers

• FedMerge: Federated Personalization via Model Merging [51.12769696559237]
  One global model might not be sufficient to serve many clients with non-IID tasks and distributions. We propose a novel FedMerge'' approach that can create a personalized model per client by simply merging multiple global models. We evaluate FedMerge on three different non-IID settings applied to different domains with diverse tasks and data types.
  arXiv  Detail & Related papers  (2025-04-09T10:44:14Z)
• Client-Centric Federated Adaptive Optimization [78.30827455292827]
  Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private. We propose Federated-Centric Adaptive Optimization, which is a class of novel federated optimization approaches.
  arXiv  Detail & Related papers  (2025-01-17T04:00:50Z)
• Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignment [4.95475852994362]
  Federated learning is a method for training a machine learning model across remote clients. We reformulate the typical federated learning setup to learn N models optimized for a common objective. We find that the technique achieves competitive results on a variety of data partitions compared to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-11-08T16:42:14Z)
• Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning [49.72857433721424]
  Vision Transformers (ViT) and Visual Prompt Tuning (VPT) achieve state-of-the-art performance with improved efficiency in various computer vision tasks. We present a novel algorithm, SGPT, that integrates Generalized FL (GFL) and Personalized FL (PFL) approaches by employing a unique combination of both shared and group-specific prompts.
  arXiv  Detail & Related papers  (2023-10-27T17:22:09Z)
• FAM: fast adaptive federated meta-learning [10.980548731600116]
  We propose a fast adaptive federated meta-learning (FAM) framework for collaboratively learning a single global model. A skeleton network is grown on each client to train a personalized model by learning additional client-specific parameters from local data. The personalized client models outperformed the locally trained models, demonstrating the efficacy of the FAM mechanism.
  arXiv  Detail & Related papers  (2023-08-26T22:54:45Z)
• 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)
• Personalizing Federated Learning with Over-the-Air Computations [84.8089761800994]
  Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients collaboratively train a global generic model under the coordination of an edge server. This paper presents a distributed training paradigm that employs analog over-the-air computation to address the communication bottleneck.
  arXiv  Detail & Related papers  (2023-02-24T08:41:19Z)
• FedNet2Net: Saving Communication and Computations in Federated Learning with Model Growing [0.0]
  Federated learning (FL) is a recently developed area of machine learning. In this paper, a novel scheme based on the notion of "model growing" is proposed. The proposed approach is tested extensively on three standard benchmarks and is shown to achieve substantial reduction in communication and client computation.
  arXiv  Detail & Related papers  (2022-07-19T21:54:53Z)
• Federated Multi-Target Domain Adaptation [99.93375364579484]
  Federated learning methods enable us to train machine learning models on distributed user data while preserving its privacy. We consider a more practical scenario where the distributed client data is unlabeled, and a centralized labeled dataset is available on the server. We propose an effective DualAdapt method to address the new challenges.
  arXiv  Detail & Related papers  (2021-08-17T17:53:05Z)
• Federated Reconstruction: Partially Local Federated Learning [4.41216624715087]
  We introduce Federated Reconstruction, the first model-agnostic framework for partially local federated learning suitable for training and inference at scale. We empirically demonstrate its performance over existing approaches for collaborative filtering and next word prediction. We describe the successful deployment of this approach at scale for federated collaborative filtering in a mobile keyboard application.
  arXiv  Detail & Related papers  (2021-02-05T23:33:43Z)
• Personalized Federated Learning with First Order Model Optimization [76.81546598985159]
  We propose an alternative to federated learning, where each client federates with other relevant clients to obtain a stronger model per client-specific objectives. We do not assume knowledge of underlying data distributions or client similarities, and allow each client to optimize for arbitrary target distributions of interest. Our method outperforms existing alternatives, while also enabling new features for personalized FL such as transfer outside of local data distributions.
  arXiv  Detail & Related papers  (2020-12-15T19:30:29Z)
• Communication-Efficient Federated Learning via Optimal Client Sampling [20.757477553095637]
  Federated learning (FL) ameliorates privacy concerns in settings where a central server coordinates learning from data distributed across many clients. We propose a novel, simple and efficient way of updating the central model in communication-constrained settings. We test this policy on a synthetic dataset for logistic regression and two FL benchmarks, namely, a classification task on EMNIST and a realistic language modeling task.
  arXiv  Detail & Related papers  (2020-07-30T02:58:00Z)

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.