Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizers

• URL: http://arxiv.org/abs/2606.06687v1
• Date: Thu, 04 Jun 2026 20:05:18 GMT
• Title: Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizers
• Abstract summary: We investigate cluster formation, involving number and composition of clusters, in decentralized federated learning (FL)<n>We propose serverless semi-decentralized FL (SSD-FL), a methodology requiring no persistent server infrastructure.<n>SSD-FL segments global rounds into intra-cluster and inter-cluster regimes, ensuring global convergence and consensus.
• Score: 49.91939413642465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate cluster formation, involving the number and composition of clusters, in decentralized federated learning (FL) with heterogeneous machine learning (ML) optimizers. While clustering in centralized FL has enabled scalability and resource savings, its value and development in fully decentralized environments have yet to be explored. Optimizing cluster formation in such environments is challenging, especially due to the complex coupling between network graph structures, local data heterogeneity, and different local ML model optimizers. To address these challenges, we propose serverless semi-decentralized FL (SSD-FL), a methodology requiring no persistent server infrastructure. In SSD-FL, cluster formation occurs via a lightweight, one-time device-to-device (D2D) initialization phase, after which actual ML model training (alongside consensus and convergence processes) is fully serverless. Functionally, SSD-FL segments global rounds into intra-cluster and inter-cluster regimes, ensuring global convergence and consensus through novel "effective loss functions" that integrate device-specific ML optimizers with network graph-based regularization. Next, SSD-FL leverages the consensus gap via the Cheeger inequality to develop an iterative clustering algorithm evaluated against our derived convergence and consensus bounds, which incorporate a unique scoring metric to quantify data and optimizer heterogeneity across devices. Finally, experimental evaluation against three categories of decentralized FL methodologies validate that SSD-FL improves both convergence speeds and communication efficiency across various network graphs, datasets, and local optimizer regimes.

Related papers

• DFCA: Decentralized Federated Clustering Algorithm [5.898448236416388]
  We introduce DFCA, a fully decentralized clustered FL algorithm that enables clients to collaboratively train cluster-specific models without central coordination.<n>DFCA uses a sequential running average to aggregate models from neighbors as updates arrive, providing a communication-efficient alternative to batch aggregation.<n>Our experiments on various datasets demonstrate that DFCA outperforms other decentralized algorithms and performs comparably to centralized IFCA.
  arXiv  Detail & Related papers  (2025-10-17T04:17:00Z)
• LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data [21.341280782748278]
  Clustered federated learning (CFL) addresses the performance challenges posed by data heterogeneity in federated learning (FL)<n>Existing CFL approaches strictly limit knowledge sharing to within clusters, lacking the integration of global knowledge with intra-cluster training.<n>We propose LCFed, an efficient CFL framework to combat these challenges.
  arXiv  Detail & Related papers  (2025-01-03T14:59:48Z)
• 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)
• Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates [71.81037644563217]
  Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. We propose straggler-aware layer-wise federated learning (SALF) that leverages the optimization procedure of NNs via backpropagation to update the global model in a layer-wise fashion.
  arXiv  Detail & Related papers  (2024-03-27T09:14:36Z)
• FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data [21.341280782748278]
  Clustered federated learning (CFL) is proposed to mitigate the performance deterioration stemming from data heterogeneity inFL. We propose an adaptive CFL framework, named FedAC, which efficiently integrates global knowledge into intra-cluster learning. Experiments show that FedAC achieves superior empirical performance, increasing the test accuracy by around 1.82% and 12.67%.
  arXiv  Detail & Related papers  (2024-03-25T06:43:28Z)
• Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization [82.12796238714589]
  We propose a novel cloud radio access network (Cloud-RAN) based vertical FL system to enable fast and accurate model aggregation. We characterize the convergence behavior of the vertical FL algorithm considering both uplink and downlink transmissions. We establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed.
  arXiv  Detail & Related papers  (2023-05-04T09:26:03Z)
• Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point [51.47520726446029]
  cooperative edge learning (CE-FL) is a distributed machine learning architecture. We model the processes taken during CE-FL, and conduct analytical training. We show the effectiveness of our framework with the data collected from a real-world testbed.
  arXiv  Detail & Related papers  (2022-03-26T00:41:57Z)
• Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks [50.68446003616802]
  Federated learning (FedL) has emerged as a popular technique for distributing model training over a set of wireless devices. We develop parallel successive learning (PSL), which expands the FedL architecture along three dimensions. Our analysis sheds light on the notion of cold vs. warmed up models, and model inertia in distributed machine learning.
  arXiv  Detail & Related papers  (2022-02-07T05:11:01Z)
• Genetic CFL: Optimization of Hyper-Parameters in Clustered Federated Learning [4.710427287359642]
  Federated learning (FL) is a distributed model for deep learning that integrates client-server architecture, edge computing, and real-time intelligence. FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of implementation due to technological limitations, communication overhead, non-IID (independent and identically distributed) data, and privacy concerns. We propose a novel hybrid algorithm, namely genetic clustered FL (Genetic CFL), that clusters edge devices based on the training hyper- parameters and genetically modifies the parameters cluster-wise.
  arXiv  Detail & Related papers  (2021-07-15T10:16:05Z)
• Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks [8.456633924613456]
  Federated learning (FL) is emerging as a new paradigm to train machine learning models in distributed systems. The paper proposes a fully distributed (or server-less) learning approach: the proposed FL algorithms leverage the cooperation of devices that perform data operations inside the network. The approach lays the groundwork for integration of FL within 5G and beyond networks characterized by decentralized connectivity and computing.
  arXiv  Detail & Related papers  (2019-12-27T15:16:04Z)

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.