A new framework trains personal digital health models using adaptive weights on support users including dissimilar ones, achieving up to 25% lower RMSE in low-data settings.
Fedbn: Federated learning on non-iid features via local batch normal- ization
9 Pith papers cite this work. Polarity classification is still indexing.
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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.
FedSPDnet uses manifold projections and retractions to average Stiefel-constrained parameters in federated SPDnet, outperforming standard federated EEGnet on EEG motor imagery benchmarks in F1 score and robustness.
A bi-level federated learning framework trains time series foundation models on heterogeneous data by enforcing domain-invariant representations locally and improving cross-domain collaboration through aware aggregation.
FedPLT assigns client-specific model layers for training and matches or beats full-model federated learning accuracy with 71-82 percent fewer trainable parameters per client.
DMM merges highly divergent domain-specific models without data sharing by synthesizing pseudo-data from normalization statistics and distilling knowledge, achieving state-of-the-art performance on unimodal and multimodal benchmarks.
DP-FLogTinyLLM combines federated learning, differential privacy, and LoRA-tuned tiny LLMs to match centralized log anomaly detection performance on Thunderbird and BGL datasets while preserving privacy.
Balanced synthetic image augmentation via GANs and diffusion models raises average AUC from 0.9206 to 0.9362 for FedAvg and 0.9429 to 0.9574 for FedProx in federated breast ultrasound classification.
A federated learning framework lets distributed weather sensors train shared deep learning models for forecasting and anomaly detection while keeping raw data private.
citing papers explorer
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Personalized Digital Health Modeling with Adaptive Support Users
A new framework trains personal digital health models using adaptive weights on support users including dissimilar ones, achieving up to 25% lower RMSE in low-data settings.
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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.
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FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet
FedSPDnet uses manifold projections and retractions to average Stiefel-constrained parameters in federated SPDnet, outperforming standard federated EEGnet on EEG motor imagery benchmarks in F1 score and robustness.
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Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach
A bi-level federated learning framework trains time series foundation models on heterogeneous data by enforcing domain-invariant representations locally and improving cross-domain collaboration through aware aggregation.
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FedPLT: Scalable, Resource-Efficient, and Heterogeneity-Aware Federated Learning via Partial Layer Training
FedPLT assigns client-specific model layers for training and matches or beats full-model federated learning accuracy with 71-82 percent fewer trainable parameters per client.
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Domain-Adaptive Model Merging Across Disconnected Modes
DMM merges highly divergent domain-specific models without data sharing by synthesizing pseudo-data from normalization statistics and distilling knowledge, achieving state-of-the-art performance on unimodal and multimodal benchmarks.
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DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs
DP-FLogTinyLLM combines federated learning, differential privacy, and LoRA-tuned tiny LLMs to match centralized log anomaly detection performance on Thunderbird and BGL datasets while preserving privacy.
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Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentation
Balanced synthetic image augmentation via GANs and diffusion models raises average AUC from 0.9206 to 0.9362 for FedAvg and 0.9429 to 0.9574 for FedProx in federated breast ultrasound classification.
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Federated Weather Modeling on Sensor Data
A federated learning framework lets distributed weather sensors train shared deep learning models for forecasting and anomaly detection while keeping raw data private.