**Technical Distributed Training Challenge: "Asynchronous Fe

Technical Distributed Training Challenge: "Asynchronous Federated Learning with Non-IID Data Across Resource-Constrained Edge Devices"

Imagine you are developing a decentralized healthcare monitoring system where multiple patients, each with their own smartphone or wearable device, contribute their ECG data to a global model for predicting heart disease. The devices are resource-limited (ARM-based CPUs), have intermittent connectivity, and are scattered across various geographical locations with diverse network conditions.

Constraints:

1. Data Non-IID: Each device has a unique distribution of ECG data, causing significant variations in the data quality and characteristics across devices.
2. Asynchronous Updates: Updates from each device are asynchronous and can be delayed due to network latency or device resource constraints.
3. Edge Device Limitations: Each device has limited CPU, memory, and storage capacity, making it difficult to perform complex computations.
4. Scalability: The system needs to scale to handle a large number of devices (at least 100).

Challenge:

Implement a fully distributed, asynchronous federated learning framework that:

1. Leverages the ECG data from resource-constrained edge devices.
2. Handles the non-IID data distribution across devices.
3. Performs efficient model updates and aggregations despite the asynchronous nature of the updates.
4. Scalably handles large numbers of devices.

Evaluation Criteria:

1. Model convergence and accuracy on a standard benchmark dataset (e.g., PhysioNet).
2. Runtime performance on a set of edge devices (e.g., Raspberry Pi, Qualcomm Snapdragon).
3. Code quality, modularity, and maintainability.
4. Ability to adapt to varying device capabilities and network conditions.

The first team to submit a working implementation will receive a research grant to further develop their solution. The community will provide support and feedback throughout the challenge.

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