Heng Pan
London, England, United Kingdom
Summary
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RockstarHeng Pan is a Lead Research Engineer specialising in federated learning, secure aggregation, and distributed ML systems with three years of focused industry and academic experience. Based in London, he advanced secure aggregation within the widely used Flower federated AI framework—contributing both client/server messaging changes and core validator logic—and led related integration work at Flower Labs. His MPhil from Cambridge underpins practical research: he implemented and accelerated multiple secure aggregation protocols (including a C++ Shamir’s Secret Sharing implementation packaged as cross-platform Python wheels) and authored Salvia+, a modular secure-aggregation library. Heng helped Cambridge win first place in the PETs Prize Challenges by designing a privacy-preserving FL solution for SWIFT anomaly detection, showing his ability to apply PETs to high-stakes financial systems. He combines hands-on backend engineering with academic rigor, optimising algorithms for production use while enabling concurrent federated algorithms over overlapping client sets. Not obviously: his work has achieved performance speedups of hundreds to thousands-fold on core secure-aggregation components, making privacy-preserving FL far more practical.
3 years of coding experience
Github Skills (15)
message-bus10
message-handler10
messaging10
python10
federated-learning10
back-end-development10
messagebox10
grpc10
protocol-buffers10
message-system10
tensorflow9
machine-learning9
distributed-systems9
pytorch9
api-design8
Programming languages (1)
Github contributions (5)
flwrlabs/flower
May 2023 - Jul 2026
Flower: A Friendly Federated AI Framework
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:1254 reviews, 680 PRs, 2911 pushes in 3 years 2 months
Contributions summary:Heng's primary contributions revolve around implementing functionalities related to secure aggregation within the "Flower" framework. Their work included adding a "SecAggMsg" field to the Task Message, indicating a focus on features to ensure secure aggregation, and adopting TaskIns/TaskRes-based communications on the client side, demonstrating their engagement with the messaging infrastructure. Additionally, the user made changes to the server-side validator code.
ai-frameworkflowerfederated-learningfederated-learning-frameworkfederated-analytics
panh99/flower
May 2023 - Jul 2023
Flower: A Friendly Federated Learning Framework
Contributions:67 pushes, 10 branches in 1 month
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