diff --git a/docs/Chapter_27_Federated_Learning_Attacks.md b/docs/Chapter_27_Federated_Learning_Attacks.md
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@@ -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