Sheaf-Based Federated Representation Learning
Gabriele D'Acunto, Enrico Grimaldi, Valeria Avino, Mario Edoardo Pandolfo, Leonardo Di Nino, Sergio Barbarossa, Paolo Di Lorenzo
cs.LG, cs.AI, cs.MA, eess.SP
Submitted: 2026-08-08
Updated: 2026-08-12
Code: https://github.com/SPAICOM/sheaf-based-federated-representation-learning
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Networks of Causal Abstractions: A Sheaf-theoretic Framework
- LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets
- Federated Learning on Riemannian Manifolds
- Think Locally, Act Globally: Federated Learning with Local and Global Representations
- SEMASIA: A Large-Scale Dataset of Semantically Structured Latent Representations
- Improving Relative Representations with Learned Anchors and Whitened Inner Products
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks