A typed tensor language formalizes federated computations via virtual global tensor semantics and proves shared-state factorization for one-round and iterative programs, plus a differentiable fragment for gradient descent.
hub
Flower: A Friendly Federated Learning Research Framework
24 Pith papers cite this work. Polarity classification is still indexing.
24
Pith papers citing it
abstract
Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud. However, FL is difficult to implement realistically, both in terms of scale and systems heterogeneity. Although there are a number of research frameworks available to simulate FL algorithms, they do not support the study of scalable FL workloads on heterogeneous edge devices. In this paper, we present Flower -- a comprehensive FL framework that distinguishes itself from existing platforms by offering new facilities to execute large-scale FL experiments and consider richly heterogeneous FL device scenarios. Our experiments show Flower can perform FL experiments up to 15M in client size using only a pair of high-end GPUs. Researchers can then seamlessly migrate experiments to real devices to examine other parts of the design space. We believe Flower provides the community with a critical new tool for FL study and development.
hub tools
citation-role summary
background 2
method 1
citation-polarity summary
representative citing papers
The FedSurg challenge benchmarks federated learning on appendectomy videos and finds only 26% F1 on unseen centers even with centralized data, plus extra penalties from decentralization, with spatiotemporal models performing best.
FeDa4Fair is a new library and benchmark for creating federated datasets with heterogeneous client-level biases to standardize evaluation of fairness methods in federated learning.
Real 5G testbed experiments show consistent stragglers in 70% of federated learning trials due to communication delays, challenging common wireless FL assumptions.
New cycle-consistent optimization, task vector theory, singular vector decompositions, adaptive routing, and efficient evolutionary search provide foundations for merging neural network weights across tasks.
Harmonization works better than personalization for appearance-based domain shifts in federated medical imaging while personalization is superior for structural shifts, with both performing similarly when shifts are small.
AW-PSP dynamically weights node sampling by real-time availability predictions and failure correlations to improve robustness, label coverage, and fairness in federated learning under correlated device failures.
CroSatFL cuts ground station communications by over 100x and transmission energy by 6x in satellite federated learning compared to baselines, while keeping competitive accuracy.
FedRouter clusters adapters locally per task samples and globally across clients to create task-centric personalized models, improving generalization and reducing task interference in federated fine-tuning.
Federated learning trains private local randomised predictors whose aggregation yields a global predictor with nonvacuous PAC-Bayesian generalisation bounds and near-centralized accuracy.
Embedding-based federated learning with personalised aggregation and governance platform improves iron deficiency prediction from full blood count data across two non-IID real-world clinical sites.
M²FedAQI is a lightweight multimodal federated framework that fuses visual and tabular data via feature modulation for improved AQI prediction and regression on heterogeneous edge devices.
FedSSG generates and shares synthetic samples within a federated setup to reduce class imbalance and domain shift problems in medical image classification.
Fed-FSTQ reduces uplink traffic by 46x and improves time-to-accuracy by 52% in federated LLM fine-tuning using Fisher-guided token quantization and selection.
OpenCLAW-Nexus uses a single discounted Beta-reputation model to unify reputation-based node selection, Rep-FedAvg aggregation, and reputation-aware BFT consensus, achieving Byzantine resilience in decentralized FL with 72.6% accuracy on non-IID CIFAR-10 under 20% attacks.
CoCoGen+ models each federated learning round as a weighted potential game with strategic synthetic data generation and payoff redistribution incentives, showing improved efficiency over baselines under non-IID data and competition.
Stacking seven black-box estimators into a meta-classifier reveals persistent membership leakage in differentially private federated learning models at epsilon=200 on NIST genomics data, outperforming single-signal baselines.
FedRef uses a temporally aggregated reference model and MAP regularization for server-side fine-tuning to reduce forgetting and drift in non-IID federated learning, showing better accuracy and lower client compute on image tasks.
Benchmarks of MPI, gRPC, and PyTorch RPC in cross-silo FL plus a new gRPC+S3 hybrid backend deliver up to 3.8x speedup for large-model transmission under realistic network conditions.
A framework automates federated learning aggregation strategy selection via LLM inference in single-trial mode and genetic search in multi-trial mode, improving robustness under non-IID data.
AI4EOSC is a federated cloud platform that integrates modular AI development, serverless AI-as-a-Service, and distributed orchestration with built-in FAIR metadata and provenance tracking for scientific AI workloads in EOSC.
Lightweight federated learning with frozen embeddings and MLP heads reaches competitive micro and macro F1 scores for ICD-9 and ICD-10 coding on MIMIC-IV, nearly matching centralized training.
DeTrigger detects and mitigates backdoor attacks in federated learning via gradient analysis and temperature scaling, claiming up to 251x faster detection and 98.9% attack reduction on four datasets with minimal accuracy loss.
Federated aggregation strategies show distinct performance trade-offs in accuracy, loss, and efficiency depending on whether client data distributions are homogeneous or heterogeneous.
citing papers explorer
-
A Typed Tensor Language for Federated Learning
A typed tensor language formalizes federated computations via virtual global tensor semantics and proves shared-state factorization for one-round and iterative programs, plus a differentiable fragment for gradient descent.
-
Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge
The FedSurg challenge benchmarks federated learning on appendectomy videos and finds only 26% F1 on unseen centers even with centralized data, plus extra penalties from decentralization, with spatiotemporal models performing best.
-
FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation
FeDa4Fair is a new library and benchmark for creating federated datasets with heterogeneous client-level biases to standardize evaluation of fairness methods in federated learning.
-
Beyond Assumptions: Measuring Federated Learning over Real 5G Networks
Real 5G testbed experiments show consistent stragglers in 70% of federated learning trials due to communication delays, challenging common wireless FL assumptions.
-
Model Merging: Foundations and Algorithms
New cycle-consistent optimization, task vector theory, singular vector decompositions, adaptive routing, and efficient evolutionary search provide foundations for merging neural network weights across tasks.
-
When To Adapt? Adapting the Model or Data in Federated Medical Imaging
Harmonization works better than personalization for appearance-based domain shifts in federated medical imaging while personalization is superior for structural shifts, with both performing similarly when shifts are small.
-
Robust Synchronisation for Federated Learning in The Face of Correlated Device Failure
AW-PSP dynamically weights node sampling by real-time availability predictions and failure correlations to improve robustness, label coverage, and fairness in federated learning under correlated device failures.
-
CroSatFL: Energy-Efficient Federated Learning with Cross-Aggregation for Satellite Edge Computing
CroSatFL cuts ground station communications by over 100x and transmission energy by 6x in satellite federated learning compared to baselines, while keeping competitive accuracy.
-
Task-Centric Personalized Federated Fine-Tuning of Language Models
FedRouter clusters adapters locally per task samples and globally across clients to create task-centric personalized models, improving generalization and reducing task interference in federated fine-tuning.
-
Federated Learning with Nonvacuous Generalisation Bounds
Federated learning trains private local randomised predictors whose aggregation yields a global predictor with nonvacuous PAC-Bayesian generalisation bounds and near-centralized accuracy.
-
Embedding-Based Federated Learning with Runtime Governance for Iron Deficiency Prediction
Embedding-based federated learning with personalised aggregation and governance platform improves iron deficiency prediction from full blood count data across two non-IID real-world clinical sites.
-
M$^2$FedAQI: Multimodal Federated Learning for Air Quality Prediction on Heterogeneous Edge Devices
M²FedAQI is a lightweight multimodal federated framework that fuses visual and tabular data via feature modulation for improved AQI prediction and regression on heterogeneous edge devices.
-
Federated Medical Image Classification under Class and Domain Imbalance exploiting Synthetic Sample Generation
FedSSG generates and shares synthetic samples within a federated setup to reduce class imbalance and domain shift problems in medical image classification.
-
FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices
Fed-FSTQ reduces uplink traffic by 46x and improves time-to-accuracy by 52% in federated LLM fine-tuning using Fisher-guided token quantization and selection.
-
OpenCLAW-Nexus: A Self-Reinforcing Trust Framework for Byzantine-Resilient Decentralized Federated Learning
OpenCLAW-Nexus uses a single discounted Beta-reputation model to unify reputation-based node selection, Rep-FedAvg aggregation, and reputation-aware BFT consensus, achieving Byzantine resilience in decentralized FL with 72.6% accuracy on non-IID CIFAR-10 under 20% attacks.
-
Cooperate to Compete: Strategic Data Generation and Incentivization Framework for Coopetitive Cross-Silo Federated Learning
CoCoGen+ models each federated learning round as a weighted potential game with strategic synthetic data generation and payoff redistribution incentives, showing improved efficiency over baselines under non-IID data and competition.
-
Evaluating Differential Privacy Against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge
Stacking seven black-box estimators into a meta-classifier reveals persistent membership leakage in differentially private federated learning models at epsilon=200 on NIST genomics data, outperforming single-signal baselines.
-
FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning
FedRef uses a temporally aggregated reference model and MAP regularization for server-side fine-tuning to reduce forgetting and drift in non-IID federated learning, showing better accuracy and lower client compute on image tasks.
-
Understanding Communication Backends in Cross-Silo Federated Learning
Benchmarks of MPI, gRPC, and PyTorch RPC in cross-silo FL plus a new gRPC+S3 hybrid backend deliver up to 3.8x speedup for large-model transmission under realistic network conditions.
-
Automating aggregation strategy selection in federated learning
A framework automates federated learning aggregation strategy selection via LLM inference in single-trial mode and genetic search in multi-trial mode, improving robustness under non-IID data.
-
AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research
AI4EOSC is a federated cloud platform that integrates modular AI development, serverless AI-as-a-Service, and distributed orchestration with built-in FAIR metadata and provenance tracking for scientific AI workloads in EOSC.
-
Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings
Lightweight federated learning with frozen embeddings and MLP heads reaches competitive micro and macro F1 scores for ICD-9 and ICD-10 coding on MIMIC-IV, nearly matching centralized training.
-
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning
DeTrigger detects and mitigates backdoor attacks in federated learning via gradient analysis and temperature scaling, claiming up to 251x faster detection and 98.9% attack reduction on four datasets with minimal accuracy loss.
-
A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributions
Federated aggregation strategies show distinct performance trade-offs in accuracy, loss, and efficiency depending on whether client data distributions are homogeneous or heterogeneous.