0% Complete
Home
/
14th International Conference on Computer and Knowledge Engineering
FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID Data
Authors :
Rasoul Jafari Gohari
1
Laya Aliahmadipour
2
Ezat Valipour
3
1- Shahid Bahonar University of Kerman
2- Shahid Bahonar University of Kerman
3- Shahid Bahonar University of Kerman
Keywords :
Federated Learning،Knowledge Distillation،Brain Tumor Classification،non-IID data
Abstract :
Brain is one the most complex organs in the human body. Due to its complexity, classification of brain tumors still poses a significant challenge, making brain tumors a particularly serious medical issue. Techniques such as Machine Learning (ML) coupled with Magnetic Resonance Imaging (MRI) have paved the way for doctors and medical institutions to classify different types of tumors. However, these techniques suffer from limitations that violate patients’ privacy. Federated Learning (FL) has recently been introduced to solve such an issue, but the FL itself suffers from limitations like communication costs and dependencies on model architecture, forcing all models to have identical architectures. In this paper, we propose FedBrain-Distill, an approach that leverages Knowledge Distillation (KD) in an FL setting that maintains the users privacy and ensures the independence of FL clients in terms of model architecture. FedBrain-Distill uses an ensemble of teachers that distill their knowledge to a simple student model. The evaluation of FedBrain-Distill demonstrated high-accuracy results for both Independent and Identically Distributed (IID) and non-IID data with substantial low communication costs on the real-world Figshare brain tumor dataset. It is worth mentioning that we used Dirichlet distribution to partition the data into IID and non-IID data. All the implementation details are accessible through our Github repository.
Papers List
List of archived papers
Distilled BERT Model In Natural Language Processing
Yazdan Zandiye Vakili - Avisa Fallah - Hedieh Sajedi
Towards Efficient Capsule Networks through Approximate Squash Function and Layer-wise Quantization
Mohsen Raji - Kimia Soroush - Amir Ghazizadeh
Improving ADHD Detection with Cost-Sensitive LightGBM
Behnam Yousefimehr - Mehdi Ghatee - Ali Heydari
A Novel Hybrid Method for Clustering Text Documents using Evolutionary Optimization
Muhammad Naderi - Maryam Amiri
Financial Market Prediction Using Deep Neural Networks with Hardware Acceleration
Dara Rahmati - Mohammad Hadi Foroughi - Ali Bagherzadeh - Mehdi Foroughi - Saeid Gorgin
Overview of Electric Vehicles Charging Stations in Smart Grids
Mohammed Wadi - Wisam Elmasry - Mohammed Jouda - Hossein Shahinzadeh - Gevork B. Gharehpetian
Leveraging the Power of Object Detection Models in Identifying Litter for a Significant Reduction in Environmental Pollution
Lim Zhen Xian - Ervin Gubin Moung - Jason Teo Tze Wi - Nordin Saad - Farashazillah Yahya - Tiong Lin Rui - Ali Farzamnia
Improving the classification of high dimensional class-imbalanced data using the Chaos particle swarm optimization with Levy Flight
Mohammad Ali Zarif - Javad Hamidzadeh
Span-prediction of Unknown Values for Long-sequence Dialogue State Tracking
Marzieh Naghdi Dorabati - Reza Ramezani - Mohammad Ali Nematbakhsh
IR-LPR: Large Scale of Iranian License Plate Recognition Dataset
Mahdi Rahmani - Melika Sabaghian - Seyyedeh Mahila Moghadami - Mohammad Mohsen Talaie - Mahdi Naghibi - Mohammad Ali Keyvanrad
more
Samin Hamayesh - Version 41.3.1