The paper presents a decentralized multitask learning framework that learns task relationships directly from distributed data. It introduces a two‑phase strategy: first estimating a generalized graph Laplacian from noisy stochastic gradient iterates, then using the learned graph to facilitate cooperative multitask diffusion learning. The authors provide theoretical analysis of Laplacian estimation error, its impact on steady‑state performance, and a topology sensitivity index, and confirm the benefits of learned task graphs through simulations.
arXiv:2608. 15256v1 Announce Type: new Abstract: Collaborative training in distributed semantic communication (DSC) networks typically relies on decentralized federated learning (DFL).
The paper introduces connectivity-aware client selection for decentralized federated learning, using spectral graph tools to choose a subset of structurally relevant neighbors for each client. By leveraging eigenvector embeddings and heat/diffusion kernel similarities, the method reduces communication overhead while maintaining or improving accuracy compared to uniform subsampling and full broadcasting. Experiments show up to a 3‑percentage‑point accuracy gain under strong data heterogeneity and a 50% reduction in transmitted messages.
The paper introduces cordial learning, a distributed training framework for agents with correlated data, where each agent’s label depends on inputs from other agents. By sharing only low‑dimensional outputs, agents train local models that extract informative signals from peers, forming a game‑theoretic loss that depends on others’ models. The authors prove convergence to a globally optimal solution for linear models and show empirical effectiveness on structured multi‑digit MNIST tasks, even in nonlinear settings.
The paper presents a large‑dimensional analysis of Generalized Total Variation Minimization (GTVMin) for least‑squares federated learning. It shows that Lipschitz observations of the global GTVMin solution concentrate in high dimensions regardless of network structure, and derives exact analytic expressions for performance under both i.i.d. and non‑i.i.d. Gaussian mixture models. These results quantify how regularization balances local adaptation against collaboration, and are validated by experiments on synthetic and real‑world data.
arXiv:2505. 09854v3 Announce Type: replace Abstract: As end-user device capability increases and demand for intelligent services at the Internet's edge rises, distributed learning has emerged as a key enabling technology for the intelligent edge.
arXiv:2511.12836v2 Announce Type: replace-cross Abstract: Sampling from a target distribution induced by training data is central to Bayesian learning, with Stochastic Gradient Langevin Dynamics (SGL...
arXiv:2605. 12998v3 Announce Type: replace Abstract: Continual graph learning (CGL) aims to learn from dynamically evolving graphs while mitigating catastrophic forgetting.
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.
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.
The paper surveys collaborative learning methods that move beyond traditional Euclidean data to graph-structured data. It reviews foundational principles for Euclidean settings—learning effectiveness, efficiency, and privacy—and then extends the discussion to graph data, presenting a taxonomy of distribution scenarios, statistical heterogeneities, and standardized problem formulations. The survey also outlines open challenges and future research directions in this emerging field.
arXiv:2608. 19914v1 Announce Type: new Abstract: Network topology inference from graph signals is central to graph signal processing with applications in neuroscience, sensor, and social networks.
arXiv:2509. 16959v5 Announce Type: replace-cross Abstract: When different objectives conflict with each other in multi-task learning, gradients begin to interfere and slow convergence, thereby potentially reducing the final model's performance.