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Pavlos S. Bouzinis, Panagiotis Radoglou-Grammatikis, Ioannis Makris, Thomas Lagkas, Vasileios Argyriou, Georgios Th. Papadopoulos, Panagiotis Sarigiannidis, George K. Karagiannidis (2024): "StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems"
Maybe give motivations about why the paper should be implemented as a baseline.
StatAvg is a method for mitigating feature shift in FL. StatAvg allows the FL clients to share their individual data statistics with the server, which then aggregates this information to produce global statistics. The latter are shared with the clients and used for universal data normalization. The technique is evaluated through applications aimed at training intrusion detection systems in a federated fashion. In the context of Flower, it demonstrates how the clients and server can exchange and aggregate arbitrary metrics, instead of just the training parameters.
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:
Hi @paulBooz , it's great you opened this issue to add your StatAvg as a Flower Baseline! Normally, baselines in Flower can reproduce the "main results of the paper". Now, this is a bit up to interpretation and varies a bit between papers... what are the experiments you'd like people be able to reproduce when running the StatAvg baseline?
Hi @jafermarq! I am planning to reproduce the StatAvg curve from Fig.3 in the paper. The implementation with Flower is complete and follows the baseline guidelines. I'am almost ready to submit a PR.
Paper
Pavlos S. Bouzinis, Panagiotis Radoglou-Grammatikis, Ioannis Makris, Thomas Lagkas, Vasileios Argyriou, Georgios Th. Papadopoulos, Panagiotis Sarigiannidis, George K. Karagiannidis (2024): "StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems"
Link
https://arxiv.org/abs/2405.13062
Maybe give motivations about why the paper should be implemented as a baseline.
StatAvg is a method for mitigating feature shift in FL. StatAvg allows the FL clients to share their individual data statistics with the server, which then aggregates this information to produce global statistics. The latter are shared with the clients and used for universal data normalization. The technique is evaluated through applications aimed at training intrusion detection systems in a federated fashion. In the context of Flower, it demonstrates how the clients and server can exchange and aggregate arbitrary metrics, instead of just the training parameters.
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: