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Gill et al., 2023, "TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance", accepted at the 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE). This baseline replicates key experiments from the paper, focusing on neuron-level provenance to identify client contributions in federated learning (FL) settings.
Maybe give motivations about why the paper should be implemented as a baseline.
TraceFL introduces a novel interpretability framework for FL by tracing neuron-level contributions from clients to the global model's predictions. This approach addresses the challenge of attributing predictions to specific clients, which is crucial for debugging, accountability, and incentivization in FL systems. By integrating TraceFL as a Flower baseline, we provide researchers with a tool to analyze and understand client contributions, enhancing transparency and trust in FL deployments.
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Implementation
To implement this baseline, it is recommended to do the following items in that order:
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I’ve just pushed an updated version that incorporates all the requirements outlined in the checklist above. Please feel free to let me know if there’s anything else missing or that could be improved. I’m happy to make the changes.
Paper
Gill et al., 2023, "TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance", accepted at the 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE). This baseline replicates key experiments from the paper, focusing on neuron-level provenance to identify client contributions in federated learning (FL) settings.
Link
https://arxiv.org/abs/2312.13632
Maybe give motivations about why the paper should be implemented as a baseline.
TraceFL introduces a novel interpretability framework for FL by tracing neuron-level contributions from clients to the global model's predictions. This approach addresses the challenge of attributing predictions to specific clients, which is crucial for debugging, accountability, and incentivization in FL systems. By integrating TraceFL as a Flower baseline, we provide researchers with a tool to analyze and understand client contributions, enhancing transparency and trust in FL deployments.
Is there something else you want to add?
No response
Implementation
To implement this baseline, it is recommended to do the following items in that order:
For first time contributors
first contribution
docPrepare - understand the scope
Verify your implementation
EXTENDED_README.md
that was created in your baseline directoryREADME.md
is ready to be run by someone that is no familiar with your code. Are all step-by-step instructions clear?README.md
and verify everything runs.The text was updated successfully, but these errors were encountered: