Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications

• URL: http://arxiv.org/abs/2506.09090v1
• Date: Tue, 10 Jun 2025 10:17:15 GMT
• Title: Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications
• Authors: Arthur Oghlukyan, Nuria Gomez Blas,
• Abstract summary: The proposed algorithm incorporates adaptive communication scheduling and delayed weight compensation to reduce synchronization frequency and communication overhead.<n>We show how these innovations improve communication efficiency, scalability, convergence, and robustness in each domain.<n>Overall, the enhanced AdaBoost exhibits markedly improved efficiency and robustness across diverse FL scenarios.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a comprehensive analysis of an enhanced asynchronous AdaBoost framework for federated learning (FL), focusing on its application across five distinct domains: computer vision on edge devices, blockchain-based model transparency, on-device mobile personalization, IoT anomaly detection, and federated healthcare diagnostics. The proposed algorithm incorporates adaptive communication scheduling and delayed weight compensation to reduce synchronization frequency and communication overhead while preserving or improving model accuracy. We examine how these innovations improve communication efficiency, scalability, convergence, and robustness in each domain. Comparative metrics including training time, communication overhead, convergence iterations, and classification accuracy are evaluated using data and estimates derived from Oghlukyan's enhanced AdaBoost framework. Empirical results show, for example, training time reductions on the order of 20-35% and communication overhead reductions of 30-40% compared to baseline AdaBoost, with convergence achieved in significantly fewer boosting rounds. Tables and charts summarize these improvements by domain. Mathematical formulations of the adaptive scheduling rule and error-driven synchronization thresholds are provided. Overall, the enhanced AdaBoost exhibits markedly improved efficiency and robustness across diverse FL scenarios, suggesting broad applicability of the approach.

Related papers

• Local adapt-then-combine algorithms for distributed nonsmooth optimization: Achieving provable communication acceleration [50.67878993903822]
  We propose a communication-efficient Adapt-Then-Combine (ATC) framework, FlexATC, unifying numerous ATC-based distributed algorithms.<n>We show for the first time that local updates provably lead to communication acceleration for ATC-based distributed algorithms.
  arXiv  Detail & Related papers  (2026-02-18T02:47:05Z)
• Enhancing Communication Efficiency in FL with Adaptive Gradient Quantization and Communication Frequency Optimization [0.0]
  Federated Learning (FL) enables participant devices to collaboratively train deep learning models without sharing their data with the server or other devices.<n>FL faces a major bottleneck due to high communication overhead from frequent model updates between devices and the server.<n>We propose a three-fold strategy to drop less important features while retaining high-value ones.
  arXiv  Detail & Related papers  (2025-09-27T17:25:44Z)
• Efficient Federated Learning with Timely Update Dissemination [54.668309196009204]
  Federated Learning (FL) has emerged as a compelling methodology for the management of distributed data.<n>We propose an efficient FL approach that capitalizes on additional downlink bandwidth resources to ensure timely update dissemination.
  arXiv  Detail & Related papers  (2025-07-08T14:34:32Z)
• Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity [0.0]
  Federated learning on heterogeneous (non-IID) client data experiences slow convergence due to client drift.<n>We propose Kuramoto-FedAvg, a federated optimization algorithm that reframes the weight aggregation step as a synchronization problem.
  arXiv  Detail & Related papers  (2025-05-26T07:16:00Z)
• Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration [66.43954501171292]
  We introduce Catalyst Acceleration and propose an acceleration Decentralized Federated Learning algorithm called DFedCata. DFedCata consists of two main components: the Moreau envelope function, which addresses parameter inconsistencies, and Nesterov's extrapolation step, which accelerates the aggregation phase. Empirically, we demonstrate the advantages of the proposed algorithm in both convergence speed and generalization performance on CIFAR10/100 with various non-iid data distributions.
  arXiv  Detail & Related papers  (2024-10-09T06:17:16Z)
• Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information [6.767885381740953]
  Federated learning has emerged as a distributed optimization paradigm. We propose a novel modified framework wherein each client locally performs a perturbed gradient step. We show that our algorithm speeds convergence up to a margin of 30 global rounds compared with FedAvg.
  arXiv  Detail & Related papers  (2024-10-07T23:14:05Z)
• FADAS: Towards Federated Adaptive Asynchronous Optimization [56.09666452175333]
  Federated learning (FL) has emerged as a widely adopted training paradigm for privacy-preserving machine learning. This paper introduces federated adaptive asynchronous optimization, named FADAS, a novel method that incorporates asynchronous updates into adaptive federated optimization with provable guarantees. We rigorously establish the convergence rate of the proposed algorithms and empirical results demonstrate the superior performance of FADAS over other asynchronous FL baselines.
  arXiv  Detail & Related papers  (2024-07-25T20:02:57Z)
• PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection [65.24854366973794]
  Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in domains such as medicine, social networks, and e-commerce. We introduce a simple method termed PREprocessing and Matching (PREM for short) to improve the efficiency of GAD. Our approach streamlines GAD, reducing time and memory consumption while maintaining powerful anomaly detection capabilities.
  arXiv  Detail & Related papers  (2023-10-18T02:59:57Z)
• Over-the-Air Federated Learning and Optimization [52.5188988624998]
  We focus on Federated learning (FL) via edge-the-air computation (AirComp) We describe the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both convex and non- convex settings. For different types of local updates that can be transmitted by edge devices (i.e., model, gradient, model difference), we reveal that transmitting in AirFedAvg may cause an aggregation error. In addition, we consider more practical signal processing schemes to improve the communication efficiency and extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes.
  arXiv  Detail & Related papers  (2023-10-16T05:49:28Z)
• Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networks [72.59891661768177]
  Federated learning (FL) enables distributed learning across edge devices while protecting data privacy. We consider a FL framework with partial model pruning and personalization to overcome these challenges. This framework splits the learning model into a global part with model pruning shared with all devices to learn data representations and a personalized part to be fine-tuned for a specific device.
  arXiv  Detail & Related papers  (2023-09-04T21:10:45Z)
• Efficient Adaptive Federated Optimization of Federated Learning for IoT [0.0]
  This paper proposes a novel efficient adaptive federated optimization (EAFO) algorithm to improve efficiency of Federated Learning (FL) FL is a distributed privacy-preserving learning framework that enables IoT devices to train global model through sharing model parameters. Experiment results show that the proposed EAFO can achieve higher accuracies faster.
  arXiv  Detail & Related papers  (2022-06-23T01:49:12Z)
• Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning [20.891460617583302]
  We show that FL can improve the policy performance of IRL in terms of training efficiency and stability. To reach a good balance between improving the model's convergence performance and reducing the required communication and computation overheads, this paper proposes a system utility function.
  arXiv  Detail & Related papers  (2022-01-30T04:04:24Z)
• Dynamic Attention-based Communication-Efficient Federated Learning [85.18941440826309]
  Federated learning (FL) offers a solution to train a global machine learning model. FL suffers performance degradation when client data distribution is non-IID. We propose a new adaptive training algorithm $textttAdaFL$ to combat this degradation.
  arXiv  Detail & Related papers  (2021-08-12T14:18:05Z)
• Federated Learning with Communication Delay in Edge Networks [5.500965885412937]
  Federated learning has received significant attention as a potential solution for distributing machine learning (ML) model training through edge networks. This work addresses an important consideration of federated learning at the network edge: communication delays between the edge nodes and the aggregator. A technique called FedDelAvg (federated delayed averaging) is developed, which generalizes the standard federated averaging algorithm to incorporate a weighting between the current local model and the delayed global model received at each device during the synchronization step.
  arXiv  Detail & Related papers  (2020-08-21T06:21:35Z)

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.