Edge-FIT: Federated Instruction Tuning of Quantized LLMs for Privacy-Preserving Smart Home Environments
Fuente:
arXiv
Saved in:
| Main Authors: | Venkatesh, Vinay, Kamanuru, Vamsidhar R, Kumar, Lav, Kothari, Nikita |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Federated Markov Imputation: Privacy-Preserving Temporal Imputation in Multi-Centric ICU Environments
by: Düsing, Christoph, et al.
Published: (2025)
by: Düsing, Christoph, et al.
Published: (2025)
Federated Continual Instruction Tuning
by: Guo, Haiyang, et al.
Published: (2025)
by: Guo, Haiyang, et al.
Published: (2025)
Preserve-Then-Quantize: Balancing Rank Budgets for Quantization Error Reconstruction in LLMs
by: Cho, Yoonjun, et al.
Published: (2026)
by: Cho, Yoonjun, et al.
Published: (2026)
SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contexts
by: Zou, Qingsong, et al.
Published: (2026)
by: Zou, Qingsong, et al.
Published: (2026)
Unifying Block-wise PTQ and Distillation-based QAT for Progressive Quantization toward 2-bit Instruction-Tuned LLMs
by: Lee, Jung Hyun, et al.
Published: (2025)
by: Lee, Jung Hyun, et al.
Published: (2025)
Enhancing Model Privacy in Federated Learning with Random Masking and Quantization
by: Xu, Zhibo, et al.
Published: (2025)
by: Xu, Zhibo, et al.
Published: (2025)
Understanding and Preserving Safety in Fine-Tuned LLMs
by: Zhang, Jiawen, et al.
Published: (2026)
by: Zhang, Jiawen, et al.
Published: (2026)
APreQEL: Adaptive Mixed Precision Quantization For Edge LLMs
by: Bouzouad, Meriem, et al.
Published: (2026)
by: Bouzouad, Meriem, et al.
Published: (2026)
From LLMs to Edge: Parameter-Efficient Fine-Tuning on Edge Devices
by: Slamanig, Georg, et al.
Published: (2025)
by: Slamanig, Georg, et al.
Published: (2025)
Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence
by: Rahman, Ratun
Published: (2025)
by: Rahman, Ratun
Published: (2025)
Investigating the Privacy Risk of Using Robot Vacuum Cleaners in Smart Environments
by: Ulsmaag, Benjamin, et al.
Published: (2024)
by: Ulsmaag, Benjamin, et al.
Published: (2024)
On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs
by: Ye, Rongguang, et al.
Published: (2025)
by: Ye, Rongguang, et al.
Published: (2025)
Self-Evolving LLMs via Continual Instruction Tuning
by: Kang, Jiazheng, et al.
Published: (2025)
by: Kang, Jiazheng, et al.
Published: (2025)
Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Air
by: Azimi-Abarghouyi, Seyed Mohammad, et al.
Published: (2024)
by: Azimi-Abarghouyi, Seyed Mohammad, et al.
Published: (2024)
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
by: Zhang, Qizheng, et al.
Published: (2025)
by: Zhang, Qizheng, et al.
Published: (2025)
Online Reinforcement Learning with Passive Memory
by: Pattanaik, Anay, et al.
Published: (2024)
by: Pattanaik, Anay, et al.
Published: (2024)
Federated Latent Factor Model for Bias-Aware Recommendation with Privacy-Preserving
by: Gao, Junxiang, et al.
Published: (2025)
by: Gao, Junxiang, et al.
Published: (2025)
A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation
by: Li, Zhiwei, et al.
Published: (2025)
by: Li, Zhiwei, et al.
Published: (2025)
Privacy-Preserved Automated Scoring using Federated Learning for Educational Research
by: Latif, Ehsan, et al.
Published: (2025)
by: Latif, Ehsan, et al.
Published: (2025)
Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models
by: Liu, Jin, et al.
Published: (2026)
by: Liu, Jin, et al.
Published: (2026)
FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices
by: Li, Changyu, et al.
Published: (2026)
by: Li, Changyu, et al.
Published: (2026)
UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs
by: Chiang, Hung-Yueh, et al.
Published: (2025)
by: Chiang, Hung-Yueh, et al.
Published: (2025)
Federated Semi-Supervised Graph Neural Networks with Prototype-Guided Pseudo-Labeling for Privacy-Preserving Gestational Diabetes Mellitus Prediction
by: Daniela, G. Victor, et al.
Published: (2026)
by: Daniela, G. Victor, et al.
Published: (2026)
TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models
by: Tanna, Aditya, et al.
Published: (2025)
by: Tanna, Aditya, et al.
Published: (2025)
Adaptive Weighted Loss for Sequential Recommendations on Sparse Domains
by: Mittal, Akshay, et al.
Published: (2025)
by: Mittal, Akshay, et al.
Published: (2025)
Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization
by: Akter, Farzana, et al.
Published: (2026)
by: Akter, Farzana, et al.
Published: (2026)
PRIVEE: Privacy-Preserving Vertical Federated Learning Against Feature Inference Attacks
by: Madabushi, Sindhuja, et al.
Published: (2025)
by: Madabushi, Sindhuja, et al.
Published: (2025)
Efficient Edge LLMs Deployment via HessianAware Quantization and CPU GPU Collaborative
by: Zhang, Tuo, et al.
Published: (2025)
by: Zhang, Tuo, et al.
Published: (2025)
Explainable AI for Securing Healthcare in IoT-Integrated 6G Wireless Networks
by: Kaur, Navneet, et al.
Published: (2025)
by: Kaur, Navneet, et al.
Published: (2025)
SparseJEPA: Sparse Representation Learning of Joint Embedding Predictive Architectures
by: Hartman, Max, et al.
Published: (2025)
by: Hartman, Max, et al.
Published: (2025)
Privacy-Preserving in Blockchain-based Federated Learning Systems
by: M., Sameera K., et al.
Published: (2024)
by: M., Sameera K., et al.
Published: (2024)
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks
by: Scheliga, Daniel, et al.
Published: (2023)
by: Scheliga, Daniel, et al.
Published: (2023)
RL-Finetuned LLMs for Privacy-Preserving Synthetic Rewriting
by: Shi, Zhan, et al.
Published: (2025)
by: Shi, Zhan, et al.
Published: (2025)
FedMomentum: Preserving LoRA Training Momentum in Federated Fine-Tuning
by: Yan, Peishen, et al.
Published: (2026)
by: Yan, Peishen, et al.
Published: (2026)
Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity
by: Chen, Xiaolu, et al.
Published: (2025)
by: Chen, Xiaolu, et al.
Published: (2025)
Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions
by: Yan, Na, et al.
Published: (2025)
by: Yan, Na, et al.
Published: (2025)
Energy Disaggregation & Appliance Identification in a Smart Home: Transfer Learning enables Edge Computing
by: Shahab, M. Hashim, et al.
Published: (2023)
by: Shahab, M. Hashim, et al.
Published: (2023)
LAPA-based Dynamic Privacy Optimization for Wireless Federated Learning in Heterogeneous Environments
by: Sun, Pengcheng, et al.
Published: (2025)
by: Sun, Pengcheng, et al.
Published: (2025)
Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing
by: Piran, Fardin Jalil, et al.
Published: (2024)
by: Piran, Fardin Jalil, et al.
Published: (2024)
Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data
by: Xu, Yukai, et al.
Published: (2024)
by: Xu, Yukai, et al.
Published: (2024)
Similar Items
-
Federated Markov Imputation: Privacy-Preserving Temporal Imputation in Multi-Centric ICU Environments
by: Düsing, Christoph, et al.
Published: (2025) -
Federated Continual Instruction Tuning
by: Guo, Haiyang, et al.
Published: (2025) -
Preserve-Then-Quantize: Balancing Rank Budgets for Quantization Error Reconstruction in LLMs
by: Cho, Yoonjun, et al.
Published: (2026) -
SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contexts
by: Zou, Qingsong, et al.
Published: (2026) -
Unifying Block-wise PTQ and Distillation-based QAT for Progressive Quantization toward 2-bit Instruction-Tuned LLMs
by: Lee, Jung Hyun, et al.
Published: (2025)