# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 3 VBDA Architectures and Core Technologies
### VBDA Core Technologies
#### 3.2.3 Federated Intelligence and Edge AI for Distributed VBDA  
The integration of FL and Edge AI has become a cornerstone for building scalable, privacy-aware intelligence in distributed VBDA systems. Rather than sending raw video to centralized servers, FL allows model training to occur locally at edge nodes where data is originally captured by sharing only model updates. This decentralized strategy minimizes bandwidth usage, enhances responsiveness, and aligns with strict data privacy regulations. As a result, it is especially well-suited for sensitive, high-volume applications like smart surveillance, healthcare monitoring, and urban mobility analytics.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Reference** & **Year** & **YOLO** & \begin{tabular}{c} **R-CNN** \\ **Family** \\ \end{tabular} & \begin{tabular}{c} **3D-CNN/** \\ **SlowFast** \\ \end{tabular} & **LSTM** & **Transformers** & **GANs** & \begin{tabular}{c} **CLIP/** \\ **VLMs** \\ \end{tabular} & \begin{tabular}{c} **SSL** \\ **Methods** \\ \end{tabular} & \
\begin{tabular}{c} **Anomaly** \\ **Models** \\ \end{tabular} &
\begin{tabular}{c} **MOT** \\ **Tackers** \\ \end{tabular} \\ \hline [PERSON] et al. [4] & 2022 & ✓ & ✓ & & & & & & & & & \\ [PERSON] et al. [6] & 2022 & & & & ✓ & & & & & & \\ [PERSON] et al. [33] & 2022 & & ✓ & & & & & & & & \\ [PERSON] et al. [132] & 2022 & ✓ & ✓ & & & & & & & ✓ & \\ [PERSON] et al. [132] & 2021 & & & & ✓ & ✓ & ✓ & ✓ & & & \\ [PERSON] et al. [134] & 2021 & ✓ & ✓ & & & & & & & & \\ [PERSON] et al. [107] & 2020 & ✓ & & & & & & & & & \\ [PERSON] et al. [118] & 2020 & ✓ & & & & & & & & & \\ [PERSON] et al. [208] & 2019 & & & & & ✓ & ✓ & & & & \\ [PERSON] et al. [30] & 2019 & & & & ✓ & ✓ & & & & & \\ [PERSON] et al. [29] & 2019 & ✓ & ✓ & ✓ & ✓ & ✓ & & & & & \\ \hline
**Total** & **14** & **11** & **2** & **13** & **8** & **5** & **4** & **3** & **4** & **3** \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(Cont.\)As summarized in Table 5, FedAvg remains the most widely adopted FL paradigm due to its simplicity and communication efficiency [137]. However, it exhibits suboptimal performance in environments characterized by non-IID data, a common scenario in real-world video deployments. To mitigate this, improved variants such as FedProx have been proposed, offering better convergence and robustness under heterogeneous data distributions. Meanwhile, FL platforms such as FedML and TensorFlow Federated (TFF) provide scalable toolkits for deploying distributed VBDA applications with modular design and cross-platform compatibility [138].  
Efficient inference at the edge requires advanced model compression techniques to accommodate resource-constrained hardware. Methods such as pruning, quantization, and knowledge distillation have proven effective in reducing model size while preserving inference accuracy [3]. Edge devices, including NVIDIA Jetson, Google Coral TPU, and Intel Movidius are frequently employed in VBDA systems to support real-time tasks such as object detection, anomaly recognition, and behavior analysis [5].  
Nonetheless, VBDA systems continue to face persistent challenges, particularly in model synchronization, resource heterogeneity, and orchestration across distributed infrastructures. Furthermore, workload balancing is complicated by diverse computational capabilities, memory footprints, and energy constraints across nodes. To address these issues, recent developments have focused on lightweight orchestration frameworks and container-based deployment tools such as KubeEdge and NVIDIA FLARE, which enable dynamic scheduling, resource-aware task allocation, and seamless edge-cloud coordination [8].