Federated learning (FL) is a collaborative learning approach where multiple data owners, referred to as clients, train a model together under the orchestration of a central server by sharing the model trained on their local datasets instead of sharing the data directly. FL enables creation of more robust models without the exposure of local datasets. However, FL by itself, does not guarantee the privacy of data, because the information extracted from the communication of FL algorithms can be accumulated and utilized to infer the private local data used for training.

We developed Argonne Privacy Preserving Federated Learning framework (APPFL), with advances in differential privacy, to enable Privacy-Preserving Federated Learning (PPFL). We enabled training of AI models in a distributed setting across multiple institutions, where sensitive data are located, with the ability to scale on supercomputing resources to help create robust, trust-worthy AI models in biomedicine and smart grid applications where data privacy is essential.

Setting up a secure federated learning experiment that needs high performance computational resources across distributed sites requires technical capabilities that may not be available for all. To lower the barrier to entry for leveraging PPFL and to enable domain experts in large institutions to utilize FL, we created the Argonne Privacy-Preserving Federated Learning as a service (APPFLx), which enables cross-silo PPFL using easy to use web interface for managing, deploying, analyzing, and visualizing PPFL experiments. 

Successful candidates will have experience in developing FL frameworks, developing benchmarks for FL frameworks, fine-tuning LLMs using FL frameworks.

Position Requirements

• Working experience in Privacy preserving Federated Learning frameworks
• Bachelor's or Master's degree in Computer Science, Machine Learning, Mathematics, or a related field, and 0+ years of experience.
• Strong programming skills in Python and experience with relevant Machine Learning frameworks (e.g., TensorFlow, PyTorch).
• Solid understanding of machine learning concepts, algorithms, and techniques, particularly in the areas of federated learning, distributed optimization, and privacy-preserving techniques.
• Experience with cryptographic protocols, secure multi-party computation, and homomorphic encryption techniques is highly desirable.
• Familiarity with data privacy regulations and compliance requirements (e.g., GDPR, HIPAA).
• Excellent problem-solving, analytical, and critical thinking skills.
• Strong communication and collaboration skills, with the ability to work effectively in a cross-functional team environment.
• Proficiency in writing clean, maintainable, and well-documented code.
• Proven ability to learn and adapt to new technologies and methodologies.
• Ability to model Argonne's core values of impact, respect, safety, integrity, and teamwork.

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