Taming the Instability: A Robust Second-Order Optimizer for Federated Learning over Non-IID Data

• URL: http://arxiv.org/abs/2603.28316v1
• Date: Mon, 30 Mar 2026 11:37:46 GMT
• Title: Taming the Instability: A Robust Second-Order Optimizer for Federated Learning over Non-IID Data
• Abstract summary: We present Federated Robust Curvature Optimization (FedRCO), a novel second-order optimization framework designed to improve convergence speed and reduce communication cost.<n>FedRCO addresses these challenges by integrating an efficient approximate curvature with a provable stability mechanism.<n>We show that FedRCO achieves superior robustness against diverse non-IID scenarios while achieving higher accuracy and faster convergence than both state-of-the-art first-order and second-order methods.
• Score: 77.19117648492104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present Federated Robust Curvature Optimization (FedRCO), a novel second-order optimization framework designed to improve convergence speed and reduce communication cost in Federated Learning systems under statistical heterogeneity. Existing second-order optimization methods are often computationally expensive and numerically unstable in distributed settings. In contrast, FedRCO addresses these challenges by integrating an efficient approximate curvature optimizer with a provable stability mechanism. Specifically, FedRCO incorporates three key components: (1) a Gradient Anomaly Monitor that detects and mitigates exploding gradients in real-time, (2) a Fail-Safe Resilience protocol that resets optimization states upon numerical instability, and (3) a Curvature-Preserving Adaptive Aggregation strategy that safely integrates global knowledge without erasing the local curvature geometry. Theoretical analysis shows that FedRCO can effectively mitigate instability and prevent unbounded updates while preserving optimization efficiency. Extensive experiments show that FedRCO achieves superior robustness against diverse non-IID scenarios while achieving higher accuracy and faster convergence than both state-of-the-art first-order and second-order methods.

Related papers

• Improving Visual Representation Alignment Generation with GRPO [51.071351994330605]
  diffusion transformers have demonstrated strong image synthesis capabilities but remain inefficient to train due to weak alignment.<n>We propose VRPO, a reinforcement-based optimization strategy that replaces REPA's static alignment loss with a generative representation policy optimization objective.<n>Our VRPO-driven training seamlessly integrates into diffusion transformers, introducing negligible cost and preserving full compatibility with SiT and DiT architectures.
  arXiv  Detail & Related papers  (2026-05-30T07:21:40Z)
• DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning [62.347535250646196]
  This paper proposes DeFRiS, a Decentralized Federated Reinforcement Learning framework for robust and scalable Silo-cooperative IoT application scheduling.<n>DeFRiS integrates three innovations: (i) an action-space-agnostic policy utilizing candidate resource scoring to enable seamless knowledge transfer across heterogeneous silos; (ii) a silo-optimized local learning mechanism combining Generalized Advantage Estimation with clipped policy updates to resolve sparse delayed reward challenges; and (iii) a Dual-Track Non-IID robust decentralized aggregation protocol leveraging gradient fingerprints for similarity-aware knowledge transfer and anomaly detection.
  arXiv  Detail & Related papers  (2026-03-16T02:02:38Z)
• Causal Direction from Convergence Time: Faster Training in the True Causal Direction [0.0]
  We introduce Causal Computational Asymmetry (CCA), a principle for causal direction identification based on optimization dynamics.<n>CCA operates in optimization-time space, distinguishing it from methods such as RESIT, IGCI, and SkewScore.<n>We further embed CCA into a broader framework termed Causal Compression Learning (CCL), which integrates graph structure learning, causal information compression, and policy optimization.
  arXiv  Detail & Related papers  (2026-02-24T21:34:57Z)
• Not All Preferences Are Created Equal: Stability-Aware and Gradient-Efficient Alignment for Reasoning Models [52.48582333951919]
  We propose a dynamic framework designed to enhance alignment reliability by maximizing the Signal-to-Noise Ratio of policy updates.<n>SAGE (Stability-Aware Gradient Efficiency) integrates a coarse-grained curriculum mechanism that refreshes candidate pools based on model competence.<n> Experiments on multiple mathematical reasoning benchmarks demonstrate that SAGE significantly accelerates convergence and outperforms static baselines.
  arXiv  Detail & Related papers  (2026-02-01T12:56:10Z)
• Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality [53.525547349715595]
  We propose a novel primal-only algorithm called Rectified Robust Policy Optimization (RRPO)<n>RRPO operates directly on the primal problem without relying on dual formulations.<n>We show convergence to an approximately optimal feasible policy with complexity matching the best-known lower bound.
  arXiv  Detail & Related papers  (2025-08-24T16:59:38Z)
• Stochastic Primal-Dual Double Block-Coordinate for Two-way Partial AUC Maximization [45.99743804547533]
  Two-way partial AUCAUC is a critical performance metric for binary classification with imbalanced data.<n>Existing algorithms for TPAUC optimization remain under-explored.<n>We introduce two innovative double-coordinate block-coordinate algorithms for TPAUC optimization.
  arXiv  Detail & Related papers  (2025-05-28T03:55:05Z)
• Refining Adaptive Zeroth-Order Optimization at Ease [24.327161891577727]
  This paper introduces Refined Adaptive Zeroth-Order Optimization (R-AdaZO)<n>We first show the untapped variance reduction effect of first moment estimate on ZO gradient estimation.<n>We then refine the second moment estimate based on these variance-reduced gradient estimates to better capture the geometry of the optimization landscape.
  arXiv  Detail & Related papers  (2025-02-03T03:10:44Z)
• RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations [8.417694229876371]
  COMBINER avoids quantization and enables direct optimization of the rate-distortion performance. We propose Robust and Enhanced COMBINER (RECOMBINER) to overcome COMBINER's limitations. We show that RECOMBINER achieves competitive results with the best INR-based methods and even outperforms autoencoder-based codecs on low-resolution images.
  arXiv  Detail & Related papers  (2023-09-29T12:27:15Z)
• Federated Distributionally Robust Optimization with Non-Convex Objectives: Algorithm and Analysis [21.913563167426872]
  Asynchronous distributed algorithm named Asynchronous Single-looP alternatIve gRadient projEction is proposed.<n>New uncertainty set, i.e., constrained D-norm uncertainty set, is developed to leverage the prior distribution and flexibly control the degree of robustness.<n> empirical studies on real-world datasets demonstrate that the proposed method can not only achieve fast convergence, but also remain robust against data as well as malicious attacks.
  arXiv  Detail & Related papers  (2023-07-25T01:56:57Z)
• Distributed Distributionally Robust Optimization with Non-Convex Objectives [24.64654924173679]
  Asynchronous distributed algorithm named Asynchronous Single-looP alternatIve gRadient projEction is proposed. New uncertainty set, i.e., constrained D-norm uncertainty set, is developed to leverage the prior distribution and flexibly control the degree of robustness. empirical studies on real-world datasets demonstrate that the proposed method can not only achieve fast convergence, but also remain robust against data as well as malicious attacks.
  arXiv  Detail & Related papers  (2022-10-14T07:39:13Z)
• Momentum Accelerates the Convergence of Stochastic AUPRC Maximization [80.8226518642952]
  We study optimization of areas under precision-recall curves (AUPRC), which is widely used for imbalanced tasks. We develop novel momentum methods with a better iteration of $O (1/epsilon4)$ for finding an $epsilon$stationary solution. We also design a novel family of adaptive methods with the same complexity of $O (1/epsilon4)$, which enjoy faster convergence in practice.
  arXiv  Detail & Related papers  (2021-07-02T16:21:52Z)

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.