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
Rishi Sharma enhanced the aidecentralized/sonar repository by developing streaming aggregation support for federated averaging, enabling more flexible and scalable federated learning experiments. He introduced configurable exponential topologies, such as one_peer_exponential and hyper_hypercube, allowing dynamic communication patterns driven by configuration. Using Python and leveraging skills in graph theory and backend development, Rishi improved the reliability of graph and configuration handling by refining import management, correcting node labeling, and removing self-loops from weight-based graphs. His work focused on refactoring and code quality, resulting in a more maintainable experimentation pipeline and reducing configuration risks for decentralized system 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
117 people
Work 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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