arXiv Machine Learning
Jun 15
Federated Learning for Feature Generalization with Convex Constraints
arXiv:2606. 14416v1 Announce Type: new Abstract: Federated learning (FL) often struggles with generalization due to heterogeneous client data.
Range Penalization: Theoretical Insights with Applications in Federated Learning
arXiv:2606. 10916v1 Announce Type: cross Abstract: This paper introduces range regularization for federated learning with linear systematic components to enhance statistical accuracy and induce cross-client regularity conducive to quantization, coding, and resource efficiency.
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arXiv AI
Jul 2
Entropy-Regularized Probabilistic Gates for Sparse Model Discovery in Scarce-Data Federated Learning
arXiv:2607. 00275v1 Announce Type: cross Abstract: Federated Learning (FL) is a distributed machine learning (ML) paradigm with collaboration among multiple clients without sharing data.
arXiv Machine Learning
Jul 7
FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering
arXiv:2607. 04170v1 Announce Type: new Abstract: Federated Learning (FL) enables decentralized training without data sharing, but suffers from statistical heterogeneity across clients, leading to client drift, poor generalization, and sharp minima compared to centralized training.
arXiv AI
Aug 25
How Much Regularization Survives Averaging? Update Masking in Federated Learning
arXiv:2608.23286v1 Announce Type: cross Abstract: Federated learning on non-IID data seeks flat minima to generalize across clients, and existing methods borrow sharpness-aware minimization from cent...
arXiv Machine Learning
Aug 27
Differentiated Aggregation to Improve Generalization in Federated Learning
The paper proposes a new federated learning approach called FedALS that reduces communication costs by varying aggregation frequencies across model layers. It derives tighter generalization bounds for one‑round and multi‑round federated learning, linking these bounds to local updates and data heterogeneity. Based on representation‑learning insights, the authors argue that infrequent aggregation of early layers and more frequent aggregation of final layers yields more generalizable models, especially in non‑iid settings, and demonstrate the method’s effectiveness experimentally.
arXiv Machine Learning
Jun 10
FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection
arXiv:2604. 24012v3 Announce Type: replace Abstract: Federated learning enables a population of clients to collaboratively train machine learning models without exchanging their raw data, but standard algorithms such as FedAvg suffer from slow convergence and high communication and memory costs in heterogeneous, resource-constrained environments.
arXiv AI
Jun 16
CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings
arXiv:2506. 22427v2 Announce Type: replace-cross Abstract: We propose CLoVE (Clustering of Loss Vector Embeddings), a novel algorithm for Clustered Federated Learning (CFL).
arXiv Machine Learning
Jun 2
Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee
arXiv:2605. 28335v2 Announce Type: replace Abstract: Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but it is highly vulnerable to Byzantine attacks.
arXiv Machine Learning
Sep 18
Distributionally Robust Federated Learning with Multi-Source Data
The paper proposes a distributionally robust federated learning framework that handles both cross-client mixture uncertainty and within-client distributional ambiguity. It constructs a global ambiguity set as a union of local ambiguity sets, allowing client-specific ambiguity radii and a client-wise separable reformulation. The authors provide a high‑probability out‑of‑sample performance guarantee, develop a penalty‑based federated algorithm, prove its convergence under milder conditions, and validate its effectiveness through simulations.
arXiv Machine Learning
Jun 24
FAIRVAR: Fair Federated Learning via Variance Regularization
arXiv:2508. 12042v3 Announce Type: replace Abstract: Federated learning (FL) allows collaborative training of machine learning models across multiple parties without sharing raw data.
arXiv Machine Learning
Aug 14
Difference-of-Convex Regularization for Graph Learning by Differentiable Programming
arXiv:2608. 12757v1 Announce Type: cross Abstract: Laplacian-regularized minimization is fundamental in signal processing and machine learning, but is limited by the dense and ill-conditioned nature of the graph Laplacian pseudoinverse.
arXiv AI
Jun 30
Federated Learning with Energy-Based Structured Probabilistic Inference
arXiv:2606. 30161v1 Announce Type: cross Abstract: Federated learning typically aggregates client updates using fixed or heuristic weighting rules, which can be suboptimal when clients have heterogeneous data and varying contributions to the global model.