Aggregation on Learnable Manifolds for Asynchronous Federated Optimization

• URL: http://arxiv.org/abs/2503.14396v3
• Date: Fri, 10 Oct 2025 14:45:11 GMT
• Title: Aggregation on Learnable Manifolds for Asynchronous Federated Optimization
• Authors: Archie Licudi, Anshul Thakur, Soheila Molaei, Danielle Belgrave, David Clifton,
• Abstract summary: We introduce a geometric framework that casts aggregation as curve learning.<n>Within this, we propose AsyncBezier, which replaces linear aggregation with low-degree curvature components.<n>We show that these gains are preserved even when other methods are allocated a higher local compute budget.
• Score: 3.8208848658169763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Asynchronous federated learning (FL) with heterogeneous clients faces two key issues: curvature-induced loss barriers encountered by standard linear parameter interpolation techniques (e.g. FedAvg) and interference from stale updates misaligned with the server's current optimisation state. To alleviate these issues, we introduce a geometric framework that casts aggregation as curve learning in a Riemannian model space and decouples trajectory selection from update conflict resolution. Within this, we propose AsyncBezier, which replaces linear aggregation with low-degree polynomial (Bezier) trajectories to bypass loss barriers, and OrthoDC, which projects delayed updates via inner product-based orthogonality to reduce interference. We establish framework-level convergence guarantees covering each variant given simple assumptions on their components. On three datasets spanning general-purpose and healthcare domains, including LEAF Shakespeare and FEMNIST, our approach consistently improves accuracy and client fairness over strong asynchronous baselines; finally, we show that these gains are preserved even when other methods are allocated a higher local compute budget.

Related papers

• A Secure and Private Distributed Bayesian Federated Learning Design [56.92336577799572]
  Distributed Federated Learning (DFL) enables decentralized model training across large-scale systems without a central parameter server.<n>DFL faces three critical challenges: privacy leakage from honest-but-curious neighbors, slow convergence due to the lack of central coordination, and vulnerability to Byzantine adversaries aiming to degrade model accuracy.<n>We propose a novel DFL framework that integrates Byzantine robustness, privacy preservation, and convergence acceleration.
  arXiv  Detail & Related papers  (2026-02-23T16:12:02Z)
• OrthAlign: Orthogonal Subspace Decomposition for Non-Interfering Multi-Objective Alignment [61.02595549125661]
  Large language model (LLM) alignment faces a critical dilemma when addressing multiple human preferences.<n>We present OrthAlign, an innovative approach to resolve gradient-level conflicts in preference alignment.<n>We show that OrthAlign achieves maximum single-preference improvements ranging from 34.61% to 50.89% after multiple-objective alignment.
  arXiv  Detail & Related papers  (2025-09-29T11:16:30Z)
• Asynchronous Federated Learning with non-convex client objective functions and heterogeneous dataset [0.9208007322096533]
  Tosampling Federated Learning (FL) enables collaborative model across decentralized devices while preserving stale data privacy.<n>Asynchronous Learning (AFL) addresses these by allowing clients to update independently, improving scalability and reducing delays synchronization.<n>Our framework accommodates variations in data power, distribution, and communication, making it practical for real world applications.
  arXiv  Detail & Related papers  (2025-08-03T09:06:42Z)
• Adaptive Deadline and Batch Layered Synchronized Federated Learning [66.93447103966439]
  Federated learning (FL) enables collaborative model training across distributed edge devices while preserving data privacy, and typically operates in a round-based synchronous manner.<n>We propose ADEL-FL, a novel framework that jointly optimize per-round deadlines and user-specific batch sizes for layer-wise aggregation.
  arXiv  Detail & Related papers  (2025-05-29T19:59:18Z)
• FedDuA: Doubly Adaptive Federated Learning [2.6108066206600555]
  Federated learning is a distributed learning framework where clients collaboratively train a global model without sharing their raw data.<n>We formalize the central server optimization procedure through the lens of mirror descent and propose a novel framework, called FedDuA.<n>We prove that our proposed doubly adaptive step-size rule is minimax optimal and provide a convergence analysis for convex objectives.
  arXiv  Detail & Related papers  (2025-05-16T11:15:27Z)
• Personalized Federated Learning under Model Dissimilarity Constraints [8.095373104009868]
  KARULA is a regularized strategy for personalized federated learning that constrains the pairwise model dissimilarities between clients based on the difference in their distributions.<n>We show theoretically that KARULA converges with smooth, possibly nonrelations losses to a neighborhood rate stationary point with O (1/K)<n>We demonstrate KARULA on synthetic and real data sets sets the effectiveness of the strategy for highly complex interrelations.
  arXiv  Detail & Related papers  (2025-05-12T13:54:55Z)
• Federated Sinkhorn [2.589644824000165]
  We investigate the potential of solving the discrete Optimal Transport problem with entropy regularization in a federated learning setting. We consider both synchronous and asynchronous variants as well as all-to-all and server-client communication protocols. We empirically demonstrate the algorithms performance on synthetic datasets and a real-world financial risk assessment application.
  arXiv  Detail & Related papers  (2025-02-10T20:29:57Z)
• Decentralized Inference for Spatial Data Using Low-Rank Models [4.168323530566095]
  This paper presents a decentralized framework tailored for parameter inference in spatial low-rank models.<n>A key obstacle arises from the spatial dependence among observations, which prevents the log-likelihood from being expressed as a summation.<n>Our approach employs a block descent method integrated with multi-consensus and dynamic consensus averaging for effective parameter optimization.
  arXiv  Detail & Related papers  (2025-02-01T04:17:01Z)
• 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 Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays [1.9766522384767224]
  underlineAsynchunderlineRonous underlineExact underlineAveraging (textscAREA)<n>textscAREA communicates model residuals rather than gradient estimates, reducing exposure to gradient inversion.<n>We are first to obtain rates that scale with the average client update frequency rather than the minimum or maximum, indicating increased robustness to outliers.
  arXiv  Detail & Related papers  (2024-05-16T14:22:49Z)
• Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees [18.24213566328972]
  Decentralized learning computation (DFL) captures FL settings where both (i) model updates and (ii) model aggregations are carried out by the clients without a central server.<n>$textttDSpodFL$, a DFL methodology built on a generalized notion of $textitsporadicity$ in both local gradient and aggregation processes.<n>$textttDSpodFL$ consistently achieves improved speeds compared with baselines under various system settings.
  arXiv  Detail & Related papers  (2024-02-05T19:02:19Z)
• Stable Nonconvex-Nonconcave Training via Linear Interpolation [51.668052890249726]
  This paper presents a theoretical analysis of linearahead as a principled method for stabilizing (large-scale) neural network training. We argue that instabilities in the optimization process are often caused by the nonmonotonicity of the loss landscape and show how linear can help by leveraging the theory of nonexpansive operators.
  arXiv  Detail & Related papers  (2023-10-20T12:45:12Z)
• 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)
• Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity [72.85248553787538]
  This paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. We formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE) Experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
  arXiv  Detail & Related papers  (2023-08-04T04:18:01Z)
• 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)
• FedAgg: Adaptive Federated Learning with Aggregated Gradients [1.5653612447564105]
  We propose an adaptive FEDerated learning algorithm called FedAgg to alleviate the divergence between the local and average model parameters and obtain a fast model convergence rate. We show that our framework is superior to existing state-of-the-art FL strategies for enhancing model performance and accelerating convergence rate under IID and Non-IID datasets.
  arXiv  Detail & Related papers  (2023-03-28T08:07:28Z)
• 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)
• Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholds [12.513477328344255]
  We propose a novel methodology for distributed model aggregations via asynchronous, event-triggered consensus iterations over a network graph topology. We demonstrate that our methodology achieves the globally optimal learning model under standard assumptions in distributed learning and graph consensus literature.
  arXiv  Detail & Related papers  (2022-04-07T20:35:37Z)
• On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning [15.300983585090794]
  We present a unifying framework for heterogeneous FL algorithms with em arbitrary adaptive online model pruning. In particular, we prove that under certain sufficient conditions, these algorithms converge to a stationary point of standard FL for general smooth cost functions. We illuminate two key factors impacting convergence: pruning-induced noise and minimum coverage index.
  arXiv  Detail & Related papers  (2022-01-27T20:43:38Z)
• Semantic Correspondence with Transformers [68.37049687360705]
  We propose Cost Aggregation with Transformers (CATs) to find dense correspondences between semantically similar images. We include appearance affinity modelling to disambiguate the initial correlation maps and multi-level aggregation. We conduct experiments to demonstrate the effectiveness of the proposed model over the latest methods and provide extensive ablation studies.
  arXiv  Detail & Related papers  (2021-06-04T14:39:03Z)
• Achieving Linear Convergence in Federated Learning under Objective and Systems Heterogeneity [24.95640915217946]
  FedLin is a simple, new algorithm that exploits past gradients and employs client-specific learning rates. We show that FedLin is the only approach that is able to match centralized convergence rates (up constants) for smooth convex, convex, and non-linear loss functions.
  arXiv  Detail & Related papers  (2021-02-14T02:47:35Z)
• Faster Non-Convex Federated Learning via Global and Local Momentum [57.52663209739171]
  textttFedGLOMO is the first (first-order) FLtexttFedGLOMO algorithm. Our algorithm is provably optimal even with communication between the clients and the server.
  arXiv  Detail & Related papers  (2020-12-07T21:05:31Z)
• Deep Magnification-Flexible Upsampling over 3D Point Clouds [103.09504572409449]
  We propose a novel end-to-end learning-based framework to generate dense point clouds. We first formulate the problem explicitly, which boils down to determining the weights and high-order approximation errors. Then, we design a lightweight neural network to adaptively learn unified and sorted weights as well as the high-order refinements.
  arXiv  Detail & Related papers  (2020-11-25T14:00:18Z)
• Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization [93.78811018928583]
  This paper provides a framework to analyze the convergence of federated heterogeneous optimization algorithms. We propose FedNova, a normalized averaging method that eliminates objective inconsistency while preserving fast error convergence.
  arXiv  Detail & Related papers  (2020-07-15T05:01:23Z)

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.