FusionNet is an AMD-native federated AI platform that enables organizations to collaboratively train and improve Large Language Models without exposing sensitive data. Instead of sending confidential information to centralized cloud servers, FusionNet sends AI to the data. Each participating device performs local fine-tuning using parameter-efficient LoRA adapters on quantized models, while only privacy-preserving model updates are exchanged. Raw datasets, documents, and user information never leave the originating device. FusionNet combines Federated Learning, Differential Privacy DP-SGD, Adaptive Federated LoRA AFLoRA, secure aggregation, and robust model update validation into a unified framework. Gradient updates are clipped, protected with calibrated noise, securely aggregated, and filtered against malicious or corrupted contributions before updating the global model. This enables multiple organizations to collaboratively build better AI models while preserving privacy, security, and regulatory compliance. Built specifically for AMD's AI ecosystem, FusionNet supports heterogeneous hardware ranging from Ryzen AI laptops and Radeon GPUs to Instinct MI300X accelerators using ROCm, PyTorch, Hugging Face, RCCL, and optimized 4-bit inference. Hardware-aware scheduling and adaptive client weighting allow devices with different compute capabilities to participate efficiently in the same federation. Beyond the learning engine, FusionNet provides real-time monitoring, telemetry dashboards, fault-tolerant orchestration, Docker deployment, secure authentication, and scalable coordination for enterprise environments. It is designed for privacy-sensitive industries including healthcare, finance, legal services, manufacturing, and government, where data sovereignty is essential. FusionNet enables collaborative AI without centralizing sensitive data.
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