Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deployment

arXiv:2608.25114 · cs.LG, cs.AI · Submitted 2026-08-25 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-08-25

Updated: 2026-08-25

Terminology

• Benchmark — 100× in this paper · explained in 2 episodes
• Federated Learning — 48× in this paper · explained in 14 episodes
• Benchmarking — 26× in this paper · explained in 3 episodes
• Aggregation — 14× in this paper · explained in 1 episode
• Core — 9× in this paper · explained in 2 episodes
• Instruction Tuning — 9× in this paper · explained in 2 episodes
• Verification — 7× in this paper · explained in 2 episodes
• AUC — 6× in this paper · explained in 2 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Adam — 4× in this paper · explained in 2 episodes
• Communication Overhead — 3× in this paper · explained in 2 episodes
• F1 Score — 3× in this paper · explained in 3 episodes
• Non-IID Data — 3× in this paper · explained in 2 episodes
• Decoupling — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• 1D CNN — 1× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Decentralized Learning — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Federated Learning (FL) — 1× in this paper · explained in 7 episodes
• Flower App Bundle — 1× in this paper
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes

Sources

• FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things
• Flower: A Friendly Federated Learning Research Framework
• LEAF: A Benchmark for Federated Settings
• FedEval: A Holistic Evaluation Framework for Federated Learning
• Automatic tagging using deep convolutional neural networks
• FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models
• FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
• FedML: A Research Library and Benchmark for Federated Machine Learning
• Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
• URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection
• UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworks
• Federated Learning: Opportunities and Challenges
• Adaptive Federated Optimization
• OpenFL: An open-source framework for Federated Learning
• NVIDIA FLARE: Federated Learning from Simulation to Real-World
• Capabilities of Gemini Models in Medicine
• FederatedScope: A Flexible Federated Learning Platform for Heterogeneity
• DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services
• Federated Learning with Non-IID Data

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