adap/flower
Nov 2024 – Nov 2024
1 Month active
Languages Used
Python
Technical Skills
Data ScienceDeep LearningFederated LearningMachine LearningPythonSoftware Engineering
Waris Gill
PROFILE
Waris Gill
Developed and delivered the FedDebug Baseline feature for the adap/flower repository, introducing a systematic debugging framework for federated learning environments. This work focused on enabling interactive debugging and automated detection of malicious clients by analyzing neuron activations, thereby addressing security and reliability challenges in distributed machine learning systems. The implementation supported multiple models, datasets, and distribution types, enhancing the robustness of federated deployments. Utilizing Python and deep learning techniques, the solution integrated seamlessly with existing federated learning workflows and laid the foundation for future enhancements. No major bug fixes were recorded during this period, with efforts concentrated on feature development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,658
Activity Months1
Your Network
84 peopleShared Repositories
36
Work History
November 2024
1 Commits • 1 Features
Nov 1, 2024Month 2024-11 — Key feature delivered: FedDebug Baseline for Federated Learning in adap/flower, enabling systematic debugging and automated malicious-client detection via neuron activations. The baseline supports multiple models, datasets, and distribution types, enhancing robustness and security of federated deployments. No major bug fixes documented in this period. Overall impact: reduces time to diagnose issues in federated setups, mitigates risk from compromised clients, and improves reliability of federated systems. Technologies/skills demonstrated: Python-based debugging framework, Federated Learning concepts, neuron-activation analysis for security, interactive debugging interfaces, and scalable baseline integration.
1 Commits • 1 Features
Nov 1, 2024Month 2024-11 — Key feature delivered: FedDebug Baseline for Federated Learning in adap/flower, enabling systematic debugging and automated malicious-client detection via neuron activations. The baseline supports multiple models, datasets, and distribution types, enhancing robustness and security of federated deployments. No major bug fixes documented in this period. Overall impact: reduces time to diagnose issues in federated setups, mitigates risk from compromised clients, and improves reliability of federated systems. Technologies/skills demonstrated: Python-based debugging framework, Federated Learning concepts, neuron-activation analysis for security, interactive debugging interfaces, and scalable baseline integration.
November 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data ScienceDeep LearningFederated LearningMachine LearningPythonSoftware Engineering
Repositories Contributed To
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