Project Background:
Federated Domain Adaptation (FDA) stems from the increasing need to deploy machine learning models across diverse and decentralized data sources while addressing challenges related to domain shifts and privacy constraints. Traditional domain adaptation techniques often assume centralized access to data, which is not practical or feasible in scenarios where data is distributed across various local devices, institutions, or domains. FDA emerges as a solution to this predicament, aiming to develop machine learning models that can adapt effectively to different domains while respecting the decentralized nature of data ownership.
This project work is rooted in the intersection of federated Learning, which enables model training on decentralized data and domain adaptation, which ensures the generalization of models across varied data distributions. By leveraging techniques that allow local fine-tuning on diverse domains and aggregating adaptations into a global model, FDA seeks to provide a privacy-preserving and scalable approach to address the challenges of domain shifts in federated environments. This project background work reflects the growing importance of machine learning applications in real-world scenarios where data privacy, distribution, and adaptability across domains are paramount considerations.
Problem Statement
- The problem in the FDA revolves around the need to adapt machine learning models to diverse and decentralized data sources without compromising privacy and data ownership.
- In traditional domain adaptation, models are trained to generalize across different datasets with a centralized approach, assuming access to all data. The challenge is to develop effective adaptation strategies that allow models to learn from and adapt to the unique characteristics of each local domain while preserving the privacy of sensitive information.
- It also encompasses addressing domain shifts, where data distributions may vary across local devices, and ensuring that the adapted models generalize well across these domains.
- FDA seeks to overcome these challenges by providing a decentralized and privacy-aware framework for training machine learning models that can adapt seamlessly to diverse data sources.
Aim and Objectives
- To develop privacy-preserving machine learning models that can adapt across diverse and decentralized data sources in addressing domain shifts of federated environments.
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Implement privacy-preserving adaptation strategies.
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Enable effective domain adaptation in decentralized settings.
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Establish decentralized learning methodologies.
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Enhance robustness to domain shifts.
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Ensure scalability in federated environments.
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Improve interpretability and transparency of the adaptation process.
Contributions to Federated Domain Adaptation
- Developing novel techniques enables machine learning models to adapt across decentralized data sources while preserving the privacy of sensitive information.
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Introduction of strategies that facilitate efficient adaptation of models to diverse data distributions in decentralized environments across different domains.
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Establishment of methodologies for decentralized Learning, allowing local devices to contribute model adaptation without the need for centralized data aggregation.
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The development of scalable solutions enables the adaptation process to handle large local devices in federated environments efficiently.
- Feature innovations enhance the interpretability and transparency of the adaptation process, providing valuable insights into how models adapt across different domains in a federated setting.
- Contributions to improving the robustness of models in the presence of domain shifts, ensuring that adapted models maintain high performance across evolving data distributions.
Deep Learning Algorithms for Federated Domain Adaptation
- Federated Learning with Neural Networks (FedNN)
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Federated Domain Adaptive Neural Networks (FDANN)
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Decentralized Domain Adaptation Networks (DDAN)
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Privacy-Preserving Federated Adversarial Training (PP-FAT)
Datasets for Federated Domain Adaptation
- Federated Medical Imaging Dataset (FedMedImg)
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Privacy-Preserving Financial Transactions Dataset (PP-FinTrans)
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Federated IoT Sensor Data (FedIoT)
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Decentralized Natural Language Processing Corpus (DecNLP)
Performance Metrics for Federated Domain Adaptation
- Domain Adaptation Accuracy (DAA)
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Privacy Preservation Index (PPI)
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Federated Generalization Score (FGS)
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Decentralized Model Robustness (DMR)
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Adaptation Efficiency Ratio (AER)
Software Tools and Technologies:
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
