From 4aa288fef6e0d5d5ab8e6f41981307c99a969cf6 Mon Sep 17 00:00:00 2001 From: shiva108 Date: Sun, 11 Jan 2026 23:42:14 +0100 Subject: [PATCH] Removeed " --- docs/Chapter_27_Federated_Learning_Attacks.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/Chapter_27_Federated_Learning_Attacks.md b/docs/Chapter_27_Federated_Learning_Attacks.md index 7965164..e5b7efd 100644 --- a/docs/Chapter_27_Federated_Learning_Attacks.md +++ b/docs/Chapter_27_Federated_Learning_Attacks.md @@ -69,12 +69,12 @@ Federated learning attacks work because the architecture makes fundamental trade #### Foundational Research -| Paper | Key Finding | Relevance | -| -------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------- | ---------------------------------------------------------------------------------- | -| [McMahan et al., 2016] "[Communication-Efficient Learning of Deep Networks from Decentralized Data](https://arxiv.org/abs/1602.05629)" | Introduced FedAvg algorithm enabling practical federated learning | Defines the baseline aggregation mechanism that most attacks target | -| [Bagdasaryan et al., 2018] "[How To Backdoor Federated Learning](https://arxiv.org/abs/1807.00459)" | Demonstrated model replacement attacks achieving 100% backdoor accuracy | Showed single malicious participant can compromise entire FL system | -| [Zhu et al., 2019] "[Deep Leakage from Gradients](https://arxiv.org/abs/1906.08935)" | Reconstructed training images from shared gradients with high fidelity | Proved FL gradient sharing leaks private information | -| [Blanchard et al., 2017] "[Byzantine-Tolerant Machine Learning](https://arxiv.org/abs/1703.02757)" | Analyzed Byzantine-robust aggregation mechanisms | Established theoretical foundations for defending against adversarial participants | +| Paper | Key Finding | Relevance | +| ------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------- | ---------------------------------------------------------------------------------- | +| [McMahan et al., 2016] [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://arxiv.org/abs/1602.05629) | Introduced FedAvg algorithm enabling practical federated learning | Defines the baseline aggregation mechanism that most attacks target | +| [Bagdasaryan et al., 2018] [How To Backdoor Federated Learning](https://arxiv.org/abs/1807.00459) | Demonstrated model replacement attacks achieving 100% backdoor accuracy | Showed single malicious participant can compromise entire FL system | +| [Zhu et al., 2019] [Deep Leakage from Gradients](https://arxiv.org/abs/1906.08935) | Reconstructed training images from shared gradients with high fidelity | Proved FL gradient sharing leaks private information | +| [Blanchard et al., 2017] [Byzantine-Tolerant Machine Learning](https://arxiv.org/abs/1703.02757) | Analyzed Byzantine-robust aggregation mechanisms | Established theoretical foundations for defending against adversarial participants | #### What This Reveals