On the public citation graph
On-device Training: A First Overview on Existing Systems
0 signed · 139 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/cpe.5786openalex-snapshot
Latency‐aware adaptive micro‐batching techniques for streamed data compression on graphics processing units
inferredcites →https://doi.org/10.1007/3-540-27139-2_7openalex-snapshot
TinyOS: An Operating System for Sensor Networks
inferredcites →https://doi.org/10.1007/978-3-319-10599-4_29openalex-snapshot
Food-101 – Mining Discriminative Components with Random Forests
inferredcites →https://doi.org/10.1007/978-981-15-3020-3_31openalex-snapshot
Android Things: A Comprehensive Solution from Things to Smart Display and Speaker
inferredcites →https://doi.org/10.1007/s10710-017-9314-zopenalex-snapshot
Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1007/s11263-021-01453-zopenalex-snapshot
Knowledge Distillation: A Survey
inferredcites →https://doi.org/10.1016/j.neunet.2019.01.012openalex-snapshot
Continual lifelong learning with neural networks: A review
inferredcites →https://doi.org/10.1016/j.tics.2016.05.004openalex-snapshot
What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated
inferredcites →https://doi.org/10.1037/0033-295x.102.3.419openalex-snapshot
Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory.
inferredcites →https://doi.org/10.1038/d41586-023-00843-2openalex-snapshot
ChatGPT: tackle the growing carbon footprint of generative AI
inferredcites →https://doi.org/10.1038/nature14236openalex-snapshot
Human-level control through deep reinforcement learning
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/access.2019.2958684openalex-snapshot
Power Consumption Analysis, Measurement, Management, and Issues: A State-of-the-Art Review of Smartphone Battery and Energy Usage
inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00286openalex-snapshot
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot
MobileNetV2: Inverted Residuals and Linear Bottlenecks
inferredcites →https://doi.org/10.1109/cvpr.2018.00907openalex-snapshot
Learning Transferable Architectures for Scalable Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2019.01152openalex-snapshot
Importance Estimation for Neural Network Pruning
inferredcites →https://doi.org/10.1109/cvprw.2014.131openalex-snapshot
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
inferredcites →https://doi.org/10.1109/fg.2018.00020openalex-snapshot
VGGFace2: A Dataset for Recognising Faces across Pose and Age
inferredcites →https://doi.org/10.1109/iccv.2015.123openalex-snapshot
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
inferredcites →https://doi.org/10.1109/iccv.2017.155openalex-snapshot
Channel Pruning for Accelerating Very Deep Neural Networks
inferredcites →https://doi.org/10.1109/iccv48922.2021.00932openalex-snapshot
Few-Shot and Continual Learning with Attentive Independent Mechanisms
inferredcites →https://doi.org/10.1109/iccvw.2013.77openalex-snapshot
3D Object Representations for Fine-Grained Categorization
inferredcites →https://doi.org/10.1109/icvgip.2008.47openalex-snapshot
Automated Flower Classification over a Large Number of Classes
inferredcites →https://doi.org/10.1109/iiswc.2015.9openalex-snapshot
Characterization and Throttling-Based Mitigation of Memory Interference for Heterogeneous Smartphones
inferredcites →https://doi.org/10.1109/ijcnn52387.2021.9533927openalex-snapshot
TinyOL: TinyML with Online-Learning on Microcontrollers
inferredcites →https://doi.org/10.1109/isscc.2014.6757323openalex-snapshot
1.1 Computing's energy problem (and what we can do about it)
inferredcites →https://doi.org/10.1109/jproc.2022.3153408openalex-snapshot
A Survey of Deep Learning on Mobile Devices: Applications, Optimizations, Challenges, and Research Opportunities
inferredcites →https://doi.org/10.1109/jsen.2022.3210773openalex-snapshot
Machine Learning for Microcontroller-Class Hardware: A Review
inferredcites →https://doi.org/10.1109/lcn.2004openalex-snapshot
29th Annual IEEE International Conference on Local Computer Networks
inferredcites →https://doi.org/10.1109/lcn.2004.38openalex-snapshot
Contiki - a lightweight and flexible operating system for tiny networked sensors
inferredcites →https://doi.org/10.1109/micro.2016.7783721openalex-snapshot
vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design
inferredcites →https://doi.org/10.1109/tc.2019.2914438openalex-snapshot
NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm
inferredcites →https://doi.org/10.1109/tmc.2019.2893250openalex-snapshot
DeepWear: Adaptive Local Offloading for On-Wearable Deep Learning
inferredcites →https://doi.org/10.1109/tnn.2004.842673openalex-snapshot
Reinforcement Learning: An Introduction
inferredcites →https://doi.org/10.1109/tnnls.2019.2918225openalex-snapshot
Continuously Constructive Deep Neural Networks
inferredcites →https://doi.org/10.1109/tpami.2010.57openalex-snapshot
Product Quantization for Nearest Neighbor Search
inferredcites →https://doi.org/10.1145/1964897.1964918openalex-snapshot
Activity recognition using cell phone accelerometers
inferredcites →https://doi.org/10.1145/3079856.3080246openalex-snapshot
In-Datacenter Performance Analysis of a Tensor Processing Unit
inferredcites →https://doi.org/10.1145/3308558.3313591openalex-snapshot
A First Look at Deep Learning Apps on Smartphones
inferredcites →https://doi.org/10.1145/3386901.3388948openalex-snapshot
Approximate query service on autonomous IoT cameras
inferredcites →https://doi.org/10.1145/3442381.3449851openalex-snapshot
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data
inferredcites →https://doi.org/10.1145/3450494openalex-snapshot
A Survey of On-Device Machine Learning
inferredcites →https://doi.org/10.1145/3485447.3512148openalex-snapshot
A Comprehensive Benchmark of Deep Learning Libraries on Mobile Devices
inferredcites →https://doi.org/10.1145/3489517openalex-snapshot
Proceedings of the 59th ACM/IEEE Design Automation Conference
inferredcites →https://doi.org/10.1145/3498361.3539765openalex-snapshot
Memory-efficient DNN training on mobile devices
inferredcites →https://doi.org/10.1145/3534678openalex-snapshot
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
inferredcites →https://doi.org/10.1145/3570361.3613297openalex-snapshot
Cost-effective On-device Continual Learning over Memory Hierarchy with Miro
inferredcites →https://doi.org/10.1145/3581791.3596852openalex-snapshot
ElasticTrainer: Speeding Up On-Device Training with Runtime Elastic Tensor Selection
inferredcites →https://doi.org/10.1145/3625687.3625804openalex-snapshot
LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms
inferredcites →https://doi.org/10.1561/0400000042openalex-snapshot
The Algorithmic Foundations of Differential Privacy
inferredcites →https://doi.org/10.2307/1422448openalex-snapshot
The Psychology of Learning and Motivation (Vol. 17)
inferredcites →https://doi.org/10.4230/lipics.cosit.2022.18openalex-snapshot
AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1502.03167openalex-snapshot
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
inferredcites →https://doi.org/10.48550/arxiv.1511.00363openalex-snapshot
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
inferredcites →https://doi.org/10.48550/arxiv.1602.07360openalex-snapshot
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
inferredcites →https://doi.org/10.48550/arxiv.1611.01578openalex-snapshot
Neural Architecture Search with Reinforcement Learning
inferredcites →https://doi.org/10.48550/arxiv.1611.06440openalex-snapshot
Pruning Convolutional Neural Networks for Resource Efficient Inference
inferredcites →https://doi.org/10.48550/arxiv.1704.04861openalex-snapshot
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
inferredcites →https://doi.org/10.48550/arxiv.1711.11294openalex-snapshot
Towards Accurate Binary Convolutional Neural Network
inferredcites →https://doi.org/10.48550/arxiv.1803.03635openalex-snapshot
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1804.03209openalex-snapshot
Google Speech Commands-Musan test set
inferredcites →https://doi.org/10.48550/arxiv.1805.00907openalex-snapshot
Glow: Graph Lowering Compiler Techniques for Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1810.11910openalex-snapshot
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
inferredcites →https://doi.org/10.48550/arxiv.1811.06965openalex-snapshot
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
inferredcites →https://doi.org/10.48550/arxiv.1812.00332openalex-snapshot
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
inferredcites →https://doi.org/10.48550/arxiv.1903.02428openalex-snapshot
Fast Graph Representation Learning with PyTorch Geometric
inferredcites →https://doi.org/10.48550/arxiv.1905.11533openalex-snapshot
CGaP: Continuous Growth and Pruning for Efficient Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1905.11722openalex-snapshot
A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation
inferredcites →https://doi.org/10.48550/arxiv.1905.12588openalex-snapshot
Meta-Learning Representations for Continual Learning
inferredcites →https://doi.org/10.48550/arxiv.1910.02653openalex-snapshot
Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization
inferredcites →https://doi.org/10.48550/arxiv.2002.12418openalex-snapshot
MNN: A Universal and Efficient Inference Engine
inferredcites →https://doi.org/10.48550/arxiv.2007.10319openalex-snapshot
MCUNet: Tiny Deep Learning on IoT Devices
inferredcites →https://doi.org/10.48550/arxiv.2110.08450openalex-snapshot
Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining
inferredcites →https://doi.org/10.5281/zenodo.6332979openalex-snapshot
Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-74640-6_16openalex-snapshot
Addressing Limitations of TinyML Approaches for AI-Enabled Ambient Intelligence
inferred← citeshttps://doi.org/10.1007/978-3-031-76770-8_7openalex-snapshot
Memory-Efficient Fine-Tuning for Foundation Models
inferred← citeshttps://doi.org/10.1007/978-3-032-17174-0_3openalex-snapshot
Edge DEvice Training TEstbed
inferred← citeshttps://doi.org/10.1007/978-3-032-31936-4_8openalex-snapshot
Continuous Online Action Detection from Egocentric Videos
inferred← citeshttps://doi.org/10.1007/s43926-025-00236-zopenalex-snapshot
Privacy-preserving federated learning approach based on Hensel’s compression and differential privacy
inferred← citeshttps://doi.org/10.1016/j.adhoc.2026.104241openalex-snapshot
SafePath: A TinyML-based on-device edge intelligence framework for real-time protection of vulnerable road users
inferred← citeshttps://doi.org/10.1016/j.icte.2024.12.003openalex-snapshot
GMT: Gzip-based Memory-efficient Time-series classification
inferred← citeshttps://doi.org/10.1016/j.icte.2025.12.005openalex-snapshot
Toward storage-aware learning with compressed data an empirical exploratory study on JPEG
inferred← citeshttps://doi.org/10.1016/j.iot.2024.101462openalex-snapshot
Mind your indices! Index hijacking attacks on collaborative unpooling autoencoder systems
inferred← citeshttps://doi.org/10.1016/j.neucom.2025.131720openalex-snapshot
AutoODL: Automated on-device learning via dynamic unfreezing and semi-supervised labeling for edge AI
inferred← citeshttps://doi.org/10.1109/access.2025.3591772openalex-snapshot
Accelerated ElasticTrainer With Elastic Layer Selection
inferred← citeshttps://doi.org/10.1109/access.2026.3665255openalex-snapshot
PiSO: Pipelined Selection Optimization Framework for Preprocessed Data and DL Models on ABP Estimation Using MIMIC-IV
inferred← citeshttps://doi.org/10.1109/access.2026.3669449openalex-snapshot
YOLO-AUG: Enhancing Wildlife Monitoring on Far-Edge Devices With Dynamic Color-Space Augmentation and Efficient Learning
inferred← citeshttps://doi.org/10.1109/access.2026.3686721openalex-snapshot
Edge Distributed Dynamic Line Rating Through On Line Learning of Radial Basis Function Neural Networks for Temperature Prediction
inferred← citeshttps://doi.org/10.1109/acdsa67686.2026.11468164openalex-snapshot
Edge to Edge: Enabling Comparative Benchmarking for on-Device Training with a TensorFlow Lite Baseline
inferred← citeshttps://doi.org/10.1109/asp-dac66049.2026.11420756openalex-snapshot
Precision-Scalable Microscaling Datapaths with Optimized Reduction Tree for Efficient NPU Integration
inferred← citeshttps://doi.org/10.1109/ecai69016.2026.11613574openalex-snapshot
Cybersecurity in Embedded Systems: A Platform-Centric Review from Microcontrollers to Edge AI Devices
inferred← citeshttps://doi.org/10.1109/icccn65249.2025.11133786openalex-snapshot
P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices
inferred← citeshttps://doi.org/10.1109/iciice69672.2026.11565453openalex-snapshot
Reinforcement Learning for a Playground on the Aurora Mobile OS: An Example Using the MuJoCo Ant Framework
inferred← citeshttps://doi.org/10.1109/icima68728.2026.11564677openalex-snapshot
Latency-Aware Hardware–Software Co-Design of an ARM–NPU Heterogeneous Edge Architecture for Real-Time Neural Speech Intelligence
inferred← citeshttps://doi.org/10.1109/islped65674.2025.11261796openalex-snapshot
Efficient Precision-Scalable Hardware for Microscaling (MX) Processing in Robotics Learning
inferred← citeshttps://doi.org/10.1109/jiot.2025.3630062openalex-snapshot
Self-Supervised Incremental Learning for Insect Segmentation and Counting on Resource-Constrained Devices
inferred← citeshttps://doi.org/10.1109/jiot.2026.3654437openalex-snapshot
Efficient On-Device Domain Learning for Keyword Spotting on Ultra-Low-Power Platforms
inferred← citeshttps://doi.org/10.1109/jiot.2026.3668885openalex-snapshot
EcoAIoT: A Survey on Ecological and Sustainable AIoT Systems With Energy Harvesting
inferred← citeshttps://doi.org/10.1109/jssc.2026.3679560openalex-snapshot
A 28-nm Digital Transpose SRAM Compute-in-Memory Macro With Accurate/Approximate Dual Mode for Floating-Point Edge Training and Inference
inferred← citeshttps://doi.org/10.1109/lcn67947.2026.11660799openalex-snapshot
On Overcoming Compute and Memory Constraints for Distributed Machine Learning at the IoT Edge
inferred← citeshttps://doi.org/10.1109/lsens.2024.3426661openalex-snapshot
Learning Pressure Sensor Drifts From Zero Deployability Budget
inferred← citeshttps://doi.org/10.1109/percomworkshops68308.2026.11585205openalex-snapshot
Re-Forward: Memory-efficient Backpropagation for Reinforcement Learning at the Edge
inferred← citeshttps://doi.org/10.1109/tce.2026.3688695openalex-snapshot
PoZS: An Intelligent Resource Management and Data Placement Scheme for Next-Generation Mobile NAND Solution
inferred← citeshttps://doi.org/10.1109/tvlsi.2025.3611822openalex-snapshot
General Compilation and Mixed-Precision Partitioning: A Combined Approach for Adaptive On-Device Learning
inferred← citeshttps://doi.org/10.1145/3701728openalex-snapshot
Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision
inferred← citeshttps://doi.org/10.1145/3774905.3794703openalex-snapshot
Statistical Analysis of 12V Power Load Uncertainty Using Conditional Probability for SDV Energy Management
inferred← citeshttps://doi.org/10.2139/ssrn.4877237openalex-snapshot
Federating Dynamic Models Using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous Clients
inferred← citeshttps://doi.org/10.23919/date69613.2026.11539322openalex-snapshot
TrainDeeploy: Hardware-Accelerated Parameter-Efficient Fine-Tuning of Small Transformer Models at the Extreme Edge
inferred← citeshttps://doi.org/10.32628/cseit2410612397openalex-snapshot
On-Device AI Models: Advancing Privacy-First Machine Learning for Mobile Applications
inferred← citeshttps://doi.org/10.3390/app16115258openalex-snapshot
Personalized Human Activity Recognition Method Based on Federated Hierarchical Clustering Learning
inferred← citeshttps://doi.org/10.3390/biomimetics11060368openalex-snapshot
Seamless Human–Computer Interaction Enabled by Wearable Biointerfaces and Intelligent Systems
inferred← citeshttps://doi.org/10.3390/electronics15102084openalex-snapshot
TinySLFL: A Flash-Endurance-Aware Federated Edge Learning Framework with Layer-Wise Delayed Aggregation for Resource-Constrained Microcontrollers
inferred← citeshttps://doi.org/10.3390/s25123679openalex-snapshot
Learning Online MEMS Calibration with Time-Varying and Memory-Efficient Gaussian Neural Topologies
inferred← citeshttps://doi.org/10.3934/math.2025706openalex-snapshot
Optimizing Tensor-Train Decomposition for efficient edge AI: Accelerated decoding via GEMM and reshape minimization
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.