Saved in:
| Main Authors: | Oda, Yuki, Ono, Yuta, Nakamura, Hiroshi, Takase, Hideki |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.09313 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning
by: Ono, Yuta, et al.
Published: (2025)
by: Ono, Yuta, et al.
Published: (2025)
I-SplitEE: Image classification in Split Computing DNNs with Early Exits
by: Bajpai, Divya Jyoti, et al.
Published: (2024)
by: Bajpai, Divya Jyoti, et al.
Published: (2024)
DistrEE: Distributed Early Exit of Deep Neural Network Inference on Edge Devices
by: Peng, Xian, et al.
Published: (2025)
by: Peng, Xian, et al.
Published: (2025)
SplitFrozen: Split Learning with Device-side Model Frozen for Fine-Tuning LLM on Heterogeneous Resource-Constrained Devices
by: Ma, Jian, et al.
Published: (2025)
by: Ma, Jian, et al.
Published: (2025)
Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions
by: Li, Zuguang, et al.
Published: (2024)
by: Li, Zuguang, et al.
Published: (2024)
Early-Exit Neural Networks with Nested Prediction Sets
by: Jazbec, Metod, et al.
Published: (2023)
by: Jazbec, Metod, et al.
Published: (2023)
SplitQuant: Layer Splitting for Low-Bit Neural Network Quantization
by: Song, Jaewoo, et al.
Published: (2025)
by: Song, Jaewoo, et al.
Published: (2025)
FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devices
by: Wang, Kaile, et al.
Published: (2026)
by: Wang, Kaile, et al.
Published: (2026)
On-Sensor Convolutional Neural Networks with Early-Exits
by: Shalby, Hazem Hesham Yousef, et al.
Published: (2025)
by: Shalby, Hazem Hesham Yousef, et al.
Published: (2025)
An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networks
by: Tajgardan, Mohsen, et al.
Published: (2026)
by: Tajgardan, Mohsen, et al.
Published: (2026)
Attention Consistency Regularization for Interpretable Early-Exit Neural Networks
by: Zhao, Yanhua
Published: (2026)
by: Zhao, Yanhua
Published: (2026)
AEBNAS: Strengthening Exit Branches in Early-Exit Networks through Hardware-Aware Neural Architecture Search
by: Robben, Oscar, et al.
Published: (2025)
by: Robben, Oscar, et al.
Published: (2025)
EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models
by: Pan, Xuchen, et al.
Published: (2024)
by: Pan, Xuchen, et al.
Published: (2024)
Bridging Efficiency and Safety: Formal Verification of Neural Networks with Early Exits
by: Elboher, Yizhak Yisrael, et al.
Published: (2025)
by: Elboher, Yizhak Yisrael, et al.
Published: (2025)
ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices
by: Zhu, Guangyu, et al.
Published: (2024)
by: Zhu, Guangyu, et al.
Published: (2024)
P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices
by: Fan, Wei, et al.
Published: (2025)
by: Fan, Wei, et al.
Published: (2025)
Modality as Heterogeneity: Node Splitting and Graph Rewiring for Multimodal Graph Learning
by: Zhang, Yihan, et al.
Published: (2026)
by: Zhang, Yihan, et al.
Published: (2026)
Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization
by: Sun, Yunrui, et al.
Published: (2024)
by: Sun, Yunrui, et al.
Published: (2024)
Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks
by: Sponner, Max, et al.
Published: (2024)
by: Sponner, Max, et al.
Published: (2024)
Recommending Pre-Trained Models for IoT Devices
by: Patil, Parth V., et al.
Published: (2024)
by: Patil, Parth V., et al.
Published: (2024)
DevPiolt: Operation Recommendation for IoT Devices at Xiaomi Home
by: Wang, Yuxiang, et al.
Published: (2025)
by: Wang, Yuxiang, et al.
Published: (2025)
Adversarial Attacks to Latent Representations of Distributed Neural Networks in Split Computing
by: Zhang, Milin, et al.
Published: (2023)
by: Zhang, Milin, et al.
Published: (2023)
SPARQ: Spiking Early-Exit Neural Networks for Energy-Efficient Edge AI
by: Patne, Parth, et al.
Published: (2026)
by: Patne, Parth, et al.
Published: (2026)
EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism
by: Chen, Yanxi, et al.
Published: (2023)
by: Chen, Yanxi, et al.
Published: (2023)
ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning
by: Meng, Chuiyang, et al.
Published: (2026)
by: Meng, Chuiyang, et al.
Published: (2026)
Dywave: Event-Aligned Dynamic Tokenization for Heterogeneous IoT Sensing Signals
by: Kimura, Tomoyoshi, et al.
Published: (2026)
by: Kimura, Tomoyoshi, et al.
Published: (2026)
Beyond Greedy Exits: Improved Early Exit Decisions for Risk Control and Reliability
by: Bajpai, Divya Jyoti, et al.
Published: (2025)
by: Bajpai, Divya Jyoti, et al.
Published: (2025)
HealSplit: Towards Self-Healing through Adversarial Distillation in Split Federated Learning
by: Xie, Yuhan, et al.
Published: (2025)
by: Xie, Yuhan, et al.
Published: (2025)
S-GRPO: Early Exit via Reinforcement Learning in Reasoning Models
by: Dai, Muzhi, et al.
Published: (2025)
by: Dai, Muzhi, et al.
Published: (2025)
DTMM: Deploying TinyML Models on Extremely Weak IoT Devices with Pruning
by: Han, Lixiang, et al.
Published: (2024)
by: Han, Lixiang, et al.
Published: (2024)
A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings
by: Lin, Xiachong, et al.
Published: (2024)
by: Lin, Xiachong, et al.
Published: (2024)
Efficient Post-Training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments
by: Sponner, Max, et al.
Published: (2024)
by: Sponner, Max, et al.
Published: (2024)
HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning
by: Youn, Jiseok, et al.
Published: (2026)
by: Youn, Jiseok, et al.
Published: (2026)
Federated Learning with Gramian Angular Fields for Privacy-Preserving ECG Classification on Heterogeneous IoT Devices
by: Elmir, Youssef, et al.
Published: (2025)
by: Elmir, Youssef, et al.
Published: (2025)
SplitLoRA: Balancing Stability and Plasticity in Continual Learning Through Gradient Space Splitting
by: Qiu, Haomiao, et al.
Published: (2025)
by: Qiu, Haomiao, et al.
Published: (2025)
Convergence Rate Maximization for Split Learning-based Control of EMG Prosthetic Devices
by: Marinova, Matea, et al.
Published: (2024)
by: Marinova, Matea, et al.
Published: (2024)
Fast AI Model Partition for Split Learning over Edge Networks
by: Li, Zuguang, et al.
Published: (2025)
by: Li, Zuguang, et al.
Published: (2025)
BaB-prob: Branch and Bound with Preactivation Splitting for Probabilistic Verification of Neural Networks
by: Wang, Fangji, et al.
Published: (2025)
by: Wang, Fangji, et al.
Published: (2025)
Merino: Entropy-driven Design for Generative Language Models on IoT Devices
by: Zhao, Youpeng, et al.
Published: (2024)
by: Zhao, Youpeng, et al.
Published: (2024)
Decoupled Split Learning via Auxiliary Loss
by: Zihad, Anower, et al.
Published: (2026)
by: Zihad, Anower, et al.
Published: (2026)
Similar Items
-
Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning
by: Ono, Yuta, et al.
Published: (2025) -
I-SplitEE: Image classification in Split Computing DNNs with Early Exits
by: Bajpai, Divya Jyoti, et al.
Published: (2024) -
DistrEE: Distributed Early Exit of Deep Neural Network Inference on Edge Devices
by: Peng, Xian, et al.
Published: (2025) -
SplitFrozen: Split Learning with Device-side Model Frozen for Fine-Tuning LLM on Heterogeneous Resource-Constrained Devices
by: Ma, Jian, et al.
Published: (2025) -
Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions
by: Li, Zuguang, et al.
Published: (2024)