aidecentralized/sonar
Nov 2024 – Nov 2024
1 Month active
Languages Used
Python
Technical Skills
Algorithm DesignBackend DevelopmentDecentralized SystemsFederated LearningGraph ManipulationGraph Theory
Rishi Sharma
PROFILE
Rishi Sharma
Worked on the aidecentralized/sonar repository to expand federated learning capabilities by introducing streaming aggregation for FedAvg and implementing configurable exponential topologies. Leveraged Python and advanced graph theory to design new topology classes, enabling dynamic communication patterns and greater experimentation flexibility. Improved backend reliability by refactoring graph handling, correcting import paths, and enhancing node labeling and indexing in DynamicGraph. Addressed configuration risks by removing self-loops from weight-based graphs and updating default parameters. Focused on maintainability and scalability, the work strengthened the codebase for decentralized systems and federated learning, supporting faster iteration and more robust topology-driven experimentation pipelines for FL workloads.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
671
Activity Months1
Your Network
128 peopleWork History
November 2024
3 Commits • 2 Features
Nov 1, 20242024-11 — Focused on expanding flexible federated learning topologies and strengthening graph/config reliability for aidecentralized/sonar. Key features: streaming aggregation for FedAvg in BaseFedAvgClient with aggregate_streaming and conditional usage in receive_and_aggregate, plus a new set of configurable exponential topologies (one_peer_exponential, hyper_hypercube, simple_base_graph, base_graph). Major bug fixes: corrected imports, improved DynamicGraph node labeling and 1-based indexing, removed self-loops from weight-based graphs, updated default max_degree to 1, and removed unused dataloader import. Impact: improved scalability and experimentation flexibility for FL workloads, reduced configuration risks, and a healthier codebase. Technologies: Python, graph/topology abstractions, streaming patterns, config-driven design, and code quality improvements.
3 Commits • 2 Features
Nov 1, 20242024-11 — Focused on expanding flexible federated learning topologies and strengthening graph/config reliability for aidecentralized/sonar. Key features: streaming aggregation for FedAvg in BaseFedAvgClient with aggregate_streaming and conditional usage in receive_and_aggregate, plus a new set of configurable exponential topologies (one_peer_exponential, hyper_hypercube, simple_base_graph, base_graph). Major bug fixes: corrected imports, improved DynamicGraph node labeling and 1-based indexing, removed self-loops from weight-based graphs, updated default max_degree to 1, and removed unused dataloader import. Impact: improved scalability and experimentation flexibility for FL workloads, reduced configuration risks, and a healthier codebase. Technologies: Python, graph/topology abstractions, streaming patterns, config-driven design, and code quality improvements.
November 2024
Activity
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Quality Metrics
Correctness83.4%
Maintainability80.0%
Architecture83.4%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Algorithm DesignBackend DevelopmentDecentralized SystemsFederated LearningGraph ManipulationGraph TheoryImport ManagementNetwork TopologyRefactoring
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