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
During November 2024, Waris developed the FedDebug Baseline feature for the adap/flower repository, focusing on systematic debugging in federated learning environments. The solution introduced an interactive framework that analyzes neuron activations to automatically detect malicious clients, addressing security and reliability challenges in distributed machine learning. Waris implemented support for multiple models, datasets, and distribution types, ensuring the baseline’s scalability and robustness. The work leveraged Python and deep learning concepts, integrating data science and software engineering practices. This feature reduced diagnostic time and mitigated risks from compromised clients, laying a solid foundation for future enhancements in federated learning deployments.
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
72 people
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
Loading activity data...
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
Overview of all repositories you've contributed to across your timeline