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.
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.
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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 Machine Learning
Jun 16
Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Data
arXiv:2601. 09304v2 Announce Type: replace Abstract: Federated Learning (FL) enables distributed learning across multiple clients without sharing raw data.
arXiv Machine Learning
Jun 30
Personalized Additive Modeling for Multi-level Federated Learning
arXiv:2405. 16472v2 Announce Type: replace Abstract: Contemporary AI faces the challenge of balancing generality with user-specific personalization.
arXiv AI
Aug 18
FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting
arXiv:2608. 14654v1 Announce Type: cross Abstract: Federated Learning (FL) is a collaborative paradigm that enables multiple devices to train a global model while preserving local data privacy.
arXiv AI
Jun 16
Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages
arXiv:2606. 16891v1 Announce Type: cross Abstract: Federated Learning is rapidly evolving beyond the exchange of traditional model weights and gradients, yet existing definitions fail to capture the full scope of modern payloads like synthetic data and federated analytics.
arXiv AI
Jul 10
FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning
arXiv:2607. 08368v1 Announce Type: new Abstract: With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning.
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
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.
arXiv Machine Learning
Aug 13
Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning
arXiv:2608. 12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local.
arXiv Machine Learning
Jun 9
Federated Large Language Models: Current Progress and Future Directions
arXiv:2409. 15723v3 Announce Type: replace Abstract: Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy and governance concerns.
Hugging Face Trending Papers
Jul 9
FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning
With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning. Although one-shot federated learning alleviates this problem by minimizing communication rounds, existing iterative fine-tuning or knowledge distillation methods still face challenges such as high server-side computational costs and hyperparameter sensitivity.