Differentiated Aggregation to Improve Generalization in Federated Learning
cs.LG, cs.AI
Submitted: 2024-04-17
Updated: 2026-08-25
Terminology
Sources
- Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data
- Federated Learning: Strategies for Improving Communication Efficiency
- Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
- Decentralized Gradient Tracking with Local Steps
- Agnostic Federated Learning
- Local SGD Converges Fast and Communicates Little
- The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication
- Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms
- Minibatch vs Local SGD for Heterogeneous Distributed Learning
- Is Local SGD Better than Minibatch SGD?
- Salvaging Federated Learning by Local Adaptation
- OPT: Open Pre-trained Transformer Language Models
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: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks