arXiv Machine Learning
Jun 10
Compositional Generative Modeling from Decentralized Data
arXiv:2606. 10153v1 Announce Type: new Abstract: Learning the compositional nature of the physical world requires joint observation of interacting factors.
Decentralized Autoregressive Generation
arXiv:2601. 03184v3 Announce Type: replace-cross Abstract: The decentralization of autoregressive generation has attracted considerable attention in recent years as a solution to scaling bottlenecks.
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arXiv Machine Learning
Sep 10
Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaboration
arXiv:2609.07312v1 Announce Type: new Abstract: This paper proposes a robust decentralized personalized federated learning method R-DPFL, that enables clients to reduce the impact of Byzantine attack...
arXiv AI
Jun 16
Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environments
arXiv:2505. 19699v2 Announce Type: replace-cross Abstract: Federated Learning (FL) is a decentralized machine learning paradigm that enables clients to collaboratively train models while preserving data privacy.
arXiv AI
Jun 2
Heterogeneous Decentralized Diffusion Models
arXiv:2603. 06741v2 Announce Type: replace-cross Abstract: Training frontier-scale diffusion models often requires substantial computational resources concentrated in tightly-coupled clusters, limiting participation to well-resourced institutions.
arXiv Machine Learning
Sep 11
Partial GFlowNet: Accelerating Convergence in Large State Spaces via Strategic Partitioning
The paper introduces Partial GFlowNet, a method that partitions a large state space into overlapping partial state spaces to accelerate convergence of Generative Flow Networks. By restricting the actor’s exploration to these smaller regions and using a heuristic to switch between them, the approach enables efficient identification of high‑reward subregions. Experiments on popular datasets show that Partial GFlowNet converges faster, produces higher‑reward candidates, and improves diversity compared to existing methods.
arXiv Machine Learning
Aug 6
Stable GFlowNets with TV Monitoring and Probabilistic Guarantees
arXiv:2605. 01729v2 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets) learn to sample states proportional to an unnormalized reward.
arXiv Machine Learning
Jun 16
Communication-Efficient Neural Tangent Kernels for Heterogeneous Decentralized Federated Learning
arXiv:2512. 12737v2 Announce Type: replace Abstract: Decentralized federated learning (DFL) enables collaborative model training without a central server, but converges slowly under statistical heterogeneity.
arXiv Machine Learning
Jun 2
Exploring and Exploiting Stability in Latent Flow Matching
arXiv:2605. 08398v2 Announce Type: replace Abstract: In this work, we show that Latent Flow-Matching (LFM) models are robust to different types of perturbations, including data reduction and model capacity shrinkage.
arXiv AI
Jul 9
Collaborative Synthetic Data Generation for Knowledge Transfer in Federated Learning
arXiv:2607. 07565v1 Announce Type: cross Abstract: One-shot federated learning (OSFL) addresses the communication overhead of federated learning by limiting training to a single round, but doing so without sacrificing model quality is non-trivial, particularly when client data distributions diverge.
arXiv Machine Learning
Aug 31
Beyond Non-IID: Learner--Client Distribution Mismatch in Federated Learning
The paper addresses the mismatch between learner and client data distributions in federated learning, noting that traditional client selection methods often ignore this misalignment. It introduces a dynamic, influence-aware client selection framework that uses a small proxy dataset to estimate each client's utility for the learner’s objective, prioritizing informative sources while mitigating noise and heterogeneity. Experiments on CIFAR-10 with heterogeneous partitions show the proposed method outperforms static and dynamic baselines, achieving faster convergence and higher accuracy.
arXiv AI
Jul 7
Decentralised Federated Learning over Temporal Networks: The Role of Heterogeneities
arXiv:2607. 03171v1 Announce Type: cross Abstract: Decentralised federated learning, based on peer-to-peer communication, is increasingly proposed for on-device training of machine learning models, promising a privacy-preserving, communication-efficient training process with no risk of single-point failure.
arXiv Machine Learning
Aug 27
Cooperative Multi-Agent Reinforcement Learning for Adaptive Aggregation in Semi-Supervised Federated Learning with non-IID Data
The paper introduces pFedMARL, a federated learning framework that uses multi‑agent reinforcement learning with TD3 to dynamically adjust client contributions and personalize models. It applies a server‑side agent to optimize global aggregation and client‑side agents to balance global and local updates, eliminating the need for pre‑training. Experiments on a semi‑supervised audio spectrogram transformer show that pFedMARL outperforms or matches FedAvg, Ditto, and local training across various non‑IID settings and against adversarial clients, improving accuracy, robustness, and fairness.