Introduction
The all-purpose With its 4th generation Intel Xeon Scalable CPUs and XPUs for the Intel Data Center GPU Flex Series 170, the ASUS ESC4000-E11 server is essential for improving federated AI capabilities across a range of sectors. Because of its architecture, which maximizes distributed AI workloads, this server is perfect for industries like healthcare and finance that value privacy, scalability, and speed.
The Test Setup
Three ASUS ESC4000-E11 server computers were used to construct the testing environment. The third ASUS ESC4000-E11 was the federated server in charge of aggregating models based on the data that each federated client had, while the other two servers functioned as federated clients. Different aggregation techniques and their possible effects on the final model in federated learning settings have been the subject of many research. By default, this test combined the gradients from the federated clients using the averaging approach.
Performance insights on the federated client hardware, especially the ASUS ESC4000-E11 with the Intel Data Center GPU Flex Series 170 for acceleration, are the main goal of this test.
The following are the main metrics assessed in this setup:
- Time spent training the model
- Model precision
- Loss of training
A thorough understanding of which hardware is most appropriate for implementing federated learning in a real-world medical setting was then provided by comparing these measures to the outcomes from Intel Xeon CPUs.
Model Inference and RA Erosion Detection
It evaluated the model’s capacity to accurately identify varying degrees of RA erosion by testing its inference performance after the federated learning procedure and the final model aggregation. Three different degrees of RA erosion severity were determined using the developed mTSS model:
- Level 0: No erosion
- Level 1: Light erosion
- Level 2: Significant erosion
Effective treatment planning is facilitated by this categorization approach, which enables precise detection of RA development based on medical imaging.
Principal Advantages of Federated AI
Improved Data Security: By supporting federated learning, the ASUS ESC4000-E11 keeps private information dispersed. Maintaining data privacy requires this decentralization, particularly in industries with a high regulatory burden. By processing data locally, the server’s architecture protects privacy, lowers the possibility of data breaches, and guarantees compliance with strict data protection laws.
Flexibility and Scalability: As federated learning networks grow, the server’s design enables it to scale effectively. Larger datasets and more complicated models are supported by this scalability, which allows businesses to expand their AI capabilities while preserving peak performance across several edge devices or institutions.
Decreased Latency: The ASUS ESC4000-E11 reduces latency during model training and updates because to its strong processing capabilities. In real-time applications like medical diagnostics, where prompt decision-making may have a big influence on results, this latency reduction is very important.
Energy Efficiency: High performance and optimal power consumption are guaranteed by the incorporation of XPUs for the Intel Data Center GPU Flex Series 170. It is a sustainable option for extensive AI deployments because of its energy efficiency, which lowers costs and improves the environment.
Organizations may create federated learning settings that are quicker, safer, and more effective by using the ASUS ESC4000-E11, which will spur innovation in AI-driven industries.
Case Study: Federated AI in Medical Imaging Diagnostics
Multiple hospitals work together to increase the precision of AI models that identify illnesses using medical images like MRIs, CT scans, and X-rays in a real-world federated AI scenario involving medical imaging diagnostics. While developing a common AI model, each institution keeps its data locally to ensure compliance with privacy laws.
Infrastructure Setup
In order to manage demanding AI workloads and support federated learning, each hospital implements the ASUS ESC4000-E11, which is outfitted with 4th generation Intel Xeon Scalable processors and XPUs for Intel Data Center GPU Flex Series 170. The hospitals may work together with this configuration without exchanging raw data.
The Federated Learning Process
Data Preparation: To guarantee that local medical imaging data never leaves a safe environment, each institution preprocesses it internally.
Local Model Training: Hospitals train AI models on local datasets utilizing ASUS ESC4000-E11 servers, which feature XPUs for Intel Data Center GPU Flex Series 170 for faster training. In order to maintain anonymity, the training procedure stays within each hospital’s infrastructure.
Model Aggregation: A central server receives the locally trained models and aggregates them to create a global model. No raw data is shared during this aggregation procedure; only model parameters are used.
Updates to the Global Model: Each hospital receives a redistribution of the global model, which now incorporates the pooled wisdom of all hospitals. Iterations and further local training are part of the cycle.
Performance and Efficiency Gains
Faster Training Times: Hospitals can rapidly converge on a highly accurate global model thanks to the ASUS ESC4000-E11’s potent hardware, which drastically cuts down on training times.
Energy-Efficient Training: By using XPUs for Intel Data Center GPU Flex Series 170, training is carried out in an energy-efficient manner, lowering operating expenses and the environmental impact.
Improved Data Security: The sophisticated security features of fourth-generation Intel Xeon processors guarantee that patient data is safe throughout the federated learning process.
Outcome and Benefits of Medical Diagnostics
With the help of the ASUS ESC4000-E11 servers, the federated AI system produces:
An very reliable and accurate AI model that can diagnose illnesses from medical pictures
A cooperative architecture that allows hospitals to get access to a variety of datasets, enhancing the generalizability of the model without jeopardizing data privacy
Quicker model iterations result in the rapid deployment of diagnostic tools, which improves patient care by enabling more precise and quicker diagnoses.
Medical organizations may cooperatively improve their AI skills by using ASUS ESC4000-E11 servers in federated learning, which will improve healthcare results while maintaining data confidentiality and privacy.
In conclusion
This white paper has shown how the ASUS ESC4000-E11 AI server’s Intel Data Center GPU Flex Series 170 may greatly speed up federated learning for practical medical applications. The technology is a powerful way to advance AI in healthcare since it improves model training durations, lowers latency, and guarantees adherence to data privacy laws. By using this gear, healthcare facilities may enhance their diagnostic skills and patient care results.
