Image de couverture de livre

Fed-Meta-Align: A Similarity-Aware Aggregation and Personalization Pipeline for Federated TinyML on Heterogeneous Data

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Macharla, Hemanth, Pal, Mayukha
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Causality and Interpretability for Electrical Distribution System faults
par: Peddi, Karthik, et autres
Publié: (2025)

MLonMCU: TinyML Benchmarking with Fast Retargeting
par: van Kempen, Philipp, et autres
Publié: (2023)

TinySV: Speaker Verification in TinyML with On-device Learning
par: Pavan, Massimo, et autres
Publié: (2024)

Optimizing LoRa for Edge Computing with TinyML Pipeline for Channel Hopping
par: Grunewald, Marla, et autres
Publié: (2024)

TinyML-Enabled IoT for Sustainable Precision Irrigation
par: Taueatsoala, Kamogelo, et autres
Publié: (2026)

TinyML for Acoustic Anomaly Detection in IoT Sensor Networks
par: Almaini, Amar, et autres
Publié: (2026)

Accelerating TinyML Inference on Microcontrollers through Approximate Kernels
par: Armeniakos, Giorgos, et autres
Publié: (2024)

Toward Attention-based TinyML: A Heterogeneous Accelerated Architecture and Automated Deployment Flow
par: Wiese, Philip, et autres
Publié: (2024)

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning
par: Yi, Liping, et autres
Publié: (2023)

TinyTNAS: GPU-Free, Time-Bound, Hardware-Aware Neural Architecture Search for TinyML Time Series Classification
par: Saha, Bidyut, et autres
Publié: (2024)

Once-for-All Channel Mixers (HYPERTINYPW): Generative Compression for TinyML
par: Shaalan, Yassien
Publié: (2026)

Optimizing TinyML: The Impact of Reduced Data Acquisition Rates for Time Series Classification on Microcontrollers
par: Samanta, Riya, et autres
Publié: (2024)

Towards Sustainable Personalized On-Device Human Activity Recognition with TinyML and Cloud-Enabled Auto Deployment
par: Saha, Bidyut, et autres
Publié: (2024)

On The Dynamic Ensemble Selection for TinyML-based Systems -- a Preliminary Study
par: Puslecki, Tobiasz, et autres
Publié: (2025)

OASI: Objective-Aware Surrogate Initialization for Multi-Objective Bayesian Optimization in TinyML Keyword Spotting
par: Garai, Soumen, et autres
Publié: (2025)

Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?
par: Zeinaty, Christophe El, et autres
Publié: (2025)

Dendron: Enhancing Human Activity Recognition with On-Device TinyML Learning
par: Shalby, Hazem Hesham Yousef, et autres
Publié: (2025)

MicroFlow: An Efficient Rust-Based Inference Engine for TinyML
par: Carnelos, Matteo, et autres
Publié: (2024)

Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML
par: Deutel, Mark, et autres
Publié: (2023)

Benchmarking Energy and Latency in TinyML: A Novel Method for Resource-Constrained AI
par: Bartoli, Pietro, et autres
Publié: (2025)

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications
par: Leslin, Jelin, et autres
Publié: (2025)

On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case
par: Dehrouyeh, Fatemeh, et autres
Publié: (2024)

DTMM: Deploying TinyML Models on Extremely Weak IoT Devices with Pruning
par: Han, Lixiang, et autres
Publié: (2024)

TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems
par: Huckelberry, Jacob, et autres
Publié: (2024)

Integration of TinyML and LargeML: A Survey of 6G and Beyond
par: Vu, Thai-Hoc, et autres
Publié: (2025)

On-device Online Learning and Semantic Management of TinyML Systems
par: Ren, Haoyu, et autres
Publié: (2024)

Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification
par: Eldeeb, Eslam, et autres
Publié: (2024)

U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT
par: Huang, Zhaolan, et autres
Publié: (2023)

What changes after deployment? A survey on On-device Learning in TinyML
par: Pavan, Massimo, et autres
Publié: (2026)

A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management
par: Sucipto, Willy, et autres
Publié: (2025)

Consolidating TinyML Lifecycle with Large Language Models: Reality, Illusion, or Opportunity?
par: Wu, Guanghan, et autres
Publié: (2025)

BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables
par: Samakovlis, Dimitrios, et autres
Publié: (2024)

msf-CNN: Patch-based Multi-Stage Fusion with Convolutional Neural Networks for TinyML
par: Huang, Zhaolan, et autres
Publié: (2025)

FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data
par: Sun, Yuxia, et autres
Publié: (2025)

SNAP-UQ: Self-supervised Next-Activation Prediction for Single-Pass Uncertainty in TinyML
par: Lamaakal, Ismail, et autres
Publié: (2025)

Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learning
par: Bao, Ziyuan, et autres
Publié: (2025)

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices
par: Suwannaphong, Thanaphon, et autres
Publié: (2024)

Hybrid Convolution and Vision Transformer NAS Search Space for TinyML Image Classification
par: Djajapermana, Mikhael, et autres
Publié: (2025)

QUTE: Quantifying Uncertainty in TinyML with Early-exit-assisted ensembles for model-monitoring
par: Ghanathe, Nikhil P, et autres
Publié: (2024)

FERMI-ML: A Flexible and Resource-Efficient Memory-In-Situ SRAM Macro for TinyML acceleration
par: Lokhande, Mukul, et autres
Publié: (2025)