aiqbench analysis
This paper introduces SSD-FL, a serverless semi-decentralized federated learning framework that eliminates persistent server infrastructure by using a one-time device-to-device initialization phase. By integrating device-specific optimizers with network graph regularization and leveraging the Cheeger inequality for clustering, the method achieves improved convergence speeds and communication efficiency across heterogeneous environments. The approach addresses a critical gap in decentralized FL by effectively managing the complex coupling between network topology, data heterogeneity, and varying optimization algorithms.
Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizers
arXiv:2606.06687v1 Announce Type: new 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.
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.
Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Networking and Internet Architecture (cs.NI); Systems and Control (eess.SY)
arXiv:2606.06687 [cs.LG]
(or arXiv:2606.06687v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.06687 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 246 words
Related coverage
Data Mesh vs. Data Fabric: Key Differences and How the Lakehouse Resolves the Debate
Databricks · Aug 25, 2026
Decentralized EM Algorithm for Gaussian Mixtures under Data Heterogeneity and Partial Labeling
arXiv · Jun 5, 2026
Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity
arXiv · Aug 4, 2026
D-FROST: Decentralized Federated pRompt-tuning via Optimal tranSporT for Non-IID and Imbalanced Data
arXiv · Sep 3, 2026