FedCCL: Federated Clustered Continual Learning Framework for Privacy-focused Energy Forecasting

• URL: http://arxiv.org/abs/2504.20282v1
• Date: Mon, 28 Apr 2025 21:51:27 GMT
• Title: FedCCL: Federated Clustered Continual Learning Framework for Privacy-focused Energy Forecasting
• Authors: Michael A. Helcig, Stefan Nastic,
• Abstract summary: FedCCL is a framework specifically designed for environments with static organizational characteristics but dynamic client availability.<n>Our approach implements an asynchronous Federated Learning protocol with a three-tier model topology.<n>We show that FedCCL offers an effective framework for privacy-preserving distributed learning, maintaining high accuracy and adaptability even with dynamic participant populations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Privacy-preserving distributed model training is crucial for modern machine learning applications, yet existing Federated Learning approaches struggle with heterogeneous data distributions and varying computational capabilities. Traditional solutions either treat all participants uniformly or require costly dynamic clustering during training, leading to reduced efficiency and delayed model specialization. We present FedCCL (Federated Clustered Continual Learning), a framework specifically designed for environments with static organizational characteristics but dynamic client availability. By combining static pre-training clustering with an adapted asynchronous FedAvg algorithm, FedCCL enables new clients to immediately profit from specialized models without prior exposure to their data distribution, while maintaining reduced coordination overhead and resilience to client disconnections. Our approach implements an asynchronous Federated Learning protocol with a three-tier model topology - global, cluster-specific, and local models - that efficiently manages knowledge sharing across heterogeneous participants. Evaluation using photovoltaic installations across central Europe demonstrates that FedCCL's location-based clustering achieves an energy prediction error of 3.93% (+-0.21%), while maintaining data privacy and showing that the framework maintains stability for population-independent deployments, with 0.14 percentage point degradation in performance for new installations. The results demonstrate that FedCCL offers an effective framework for privacy-preserving distributed learning, maintaining high accuracy and adaptability even with dynamic participant populations.

Related papers

• Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation [4.869042695112397]
  Federated learning (FL) has emerged as a promising paradigm in machine learning.<n>In FL, a global model is trained iteratively on local datasets residing on individual devices.<n>This paper introduces a novel FL framework, ClusterGuardFL, that employs dissimilarity scores, k-means clustering, and reconciliation confidence scores to dynamically assign weights to client updates.
  arXiv  Detail & Related papers  (2025-03-29T04:29:24Z)
• Interaction-Aware Gaussian Weighting for Clustered Federated Learning [58.92159838586751]
  Federated Learning (FL) emerged as a decentralized paradigm to train models while preserving privacy.<n>We propose a novel clustered FL method, FedGWC (Federated Gaussian Weighting Clustering), which groups clients based on their data distribution.<n>Our experiments on benchmark datasets show that FedGWC outperforms existing FL algorithms in cluster quality and classification accuracy.
  arXiv  Detail & Related papers  (2025-02-05T16:33:36Z)
• 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.<n>We propose Federated-Centric Adaptive Optimization, which is a class of novel federated optimization approaches.
  arXiv  Detail & Related papers  (2025-01-17T04:00:50Z)
• Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine Learning [0.9208007322096533]
  Federated Learning (FL) has emerged as a powerful paradigm for decentralized machine learning, enabling collaborative model training across diverse clients without sharing raw data.<n>Traditional FL approaches often face limitations in scalability and efficiency due to their reliance on synchronous client updates.<n>We propose an Asynchronous Federated Learning (AFL) algorithm, which allows clients to update the global model independently and asynchronously.
  arXiv  Detail & Related papers  (2024-12-23T17:11:02Z)
• Personalized Federated Learning for Cross-view Geo-localization [49.40531019551957]
  We propose a methodology combining Federated Learning (FL) with Cross-view Image Geo-localization (CVGL) techniques. Our method implements a coarse-to-fine approach, where clients share only the coarse feature extractors while keeping fine-grained features specific to local environments. Results demonstrate that our federated CVGL method achieves performance close to centralized training while maintaining data privacy.
  arXiv  Detail & Related papers  (2024-11-07T13:25:52Z)
• FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning [18.38030098837294]
  Federated learning is a framework for distributed clients to collaboratively train a machine learning model using local data. We propose FedSPD, an efficient personalized federated learning algorithm for the decentralized setting. We show that FedSPD learns accurate models even in low-connectivity networks.
  arXiv  Detail & Related papers  (2024-10-24T15:48:34Z)
• FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering [28.057411252785176]
  Federated learning (FL) is an emerging distributed machine learning paradigm enabling collaborative model training on decentralized devices without exposing their local data. This paper proposes FedClust, a novel CFL approach leveraging correlations between local model weights and client data distributions.
  arXiv  Detail & Related papers  (2024-03-07T01:50:36Z)
• 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)
• Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity [14.313847382199059]
  Federated quantization-based self-supervised learning scheme (Fed-QSSL) designed to address heterogeneity in FL systems. Fed-QSSL deploys de-quantization, weighted aggregation and re-quantization, ultimately creating models personalized to both data distribution and specific infrastructure of each client's device.
  arXiv  Detail & Related papers  (2023-12-20T19:11:19Z)
• Contrastive encoder pre-training-based clustered federated learning for heterogeneous data [17.580390632874046]
  Federated learning (FL) enables distributed clients to collaboratively train a global model while preserving their data privacy. We propose contrastive pre-training-based clustered federated learning (CP-CFL) to improve the model convergence and overall performance of FL systems.
  arXiv  Detail & Related papers  (2023-11-28T05:44:26Z)
• 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)
• Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction [76.26710990597498]
  We show that the class-imbalance of the grouped data from randomly selected clients can lead to significant performance degradation. Based on our key observation, we design an efficient client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS) In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way.
  arXiv  Detail & Related papers  (2022-09-30T05:42:56Z)

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.