# ARM CCA for Edge Devices

class ARMCCAExplainer:
    """ARM CCA provides hardware TEE for edge devices."""

    def arm_cca_architecture(self):
        """ARM CCA Security Levels."""
        pass


# Example: Edge device with ARM CCA
arm_cca_device = {
    "cpu": "ARM Cortex-A78 (ARMv9-A)",
    "cores": 8,
    "ram": "16GB (8GB allocable to Realm)",
    "tee_type": "ARM CCA",
    "attestation": "ARM CCA token",
    "use_case": "Smart camera with confidential face detection",
}

# Edge GPU with Confidential Computing
class EdgeGPUTEE:
    """Edge GPU TEE Specifications (Expected 2026)"""
    def deployment_example(self):
        """Example: Medical imaging device"""
        pass

# Fully Local Confidential AI
from phala_edge import EdgeTEERuntime

class PrivateSecurityCamera:
    def __init__(self):
        # Initialize ARM CCA TEE runtime
        self.tee = EdgeTEERuntime(tee_type="arm_cca", realm_memory_mb=4096)
        # Load model into TEE Realm
        self.model = self.tee.load_model(model_path="/secure_storage/yolo_v8.onnx")
        # Verify TEE is active
        attestation = self.tee.generate_attestation()
        print(f"Camera TEE active: {attestation['tee_type']}")

    def process_frame(self, camera_frame):
        # Inference happens entirely in ARM CCA Realm
        detections = self.tee.run_inference(model=self.model, input_data=camera_frame)
        return detections

# Confidential Federated Learning: Edge Devices
class ConfidentialEdgeFederatedLearning:
    def __init__(self, device_id: str):
        self.device_id = device_id
        self.edge_tee = EdgeTEERuntime(tee_type="arm_cca")

    def local_training_round(self, local_data):
        # Train on local data in edge TEE
        model_update = self.edge_tee.train_model(base_model="medical_imaging_model_v5", local_data=local_data)
        return model_update

# Use Case: Portable Ultrasound with Confidential AI
class ConfidentialMedicalDevice:
    def __init__(self):
        self.tee = EdgeTEERuntime(tee_type="arm_cca_gpu", realm_memory_mb=8192)

    def process_ultrasound_scan(self, scan_image):
        # Real-time ultrasound analysis
        segmentation_result = self.tee.run_inference(model=self.model, input_data=scan_image)
        return segmentation_result

# Use Case: Factory Predictive Maintenance
class ConfidentialIndustrialAI:
    def __init__(self, sensor_id: str):
        self.sensor_id = sensor_id
        self.tee = EdgeTEERuntime(tee_type="arm_cca")

    def monitor_equipment(self, sensor_readings: dict):
        # Confidential predictive maintenance
        prediction = self.tee.run_inference(model=self.model, input_data=sensor_readings)
        return prediction

# Use Case: Autonomous Vehicle Perception
class ConfidentialAutonomousPerception:
    def __init__(self):
        self.tee = EdgeTEERuntime(tee_type="nvidia_jetson_orin_tee", gpu_memory_mb=32768)

    def process_camera_frame(self, camera_frames: dict):
        # Real-time perception inference
        perception_result = self.tee.run_inference(model=self.perception_model, input_data=camera_frames)
        return perception_result

# Edge TEE Deployment Workflow
from phala_edge_sdk import EdgeDeployer

class EdgeAIDeployment:
    def __init__(self):
        self.deployer = EdgeDeployer()

    def deploy_to_edge_device(self, device_ip: str):
        # Complete edge deployment workflow
        optimized_model = self.deployer.optimize_for_edge(model_path="yolo_v8_large.onnx", target_hardware="arm_cca")
        tee_package = self.deployer.create_tee_package(model=optimized_model, tee_config={"tee_type": "arm_cca"})
        deployment = self.deployer.deploy(package=tee_package, target_device=device_ip)
        return deployment

# Edge TEE Fleet Management
class EdgeTEEFleetManager:
    def __init__(self):
        self.devices = {}

    def register_device(self, device_id: str, device_ip: str):
        # Register new edge device to fleet
        pass

    def continuous_attestation_monitoring(self):
        # Continuously verify all devices
        pass

# Edge Inference Latency Optimization
class EdgeLatencyOptimizer:
    def optimize_for_realtime(self):
        # Optimization techniques for edge TEE
        combined_result = {
            "baseline_latency_ms": 45,
            "optimized_latency_ms": 8,
            "latency_reduction": "82%",
            "accuracy_loss": "2.5%",
            "tee_overhead": "5%",
            "final_latency_ms": 8.4
        }
        return combined_result