Moving LLM workloads from a single data center to a decentralized network changes the risk profile fundamentally. Instead of a controlled environment, you face a wide surface of untrusted nodes. This introduces the risk of model poisoning, where a malicious actor subtly alters training data to bake in biases or backdoors. They can also attempt model extraction attacks, sniffing the gradients or weights as they move across the public internet to steal intellectual property.
Data privacy becomes a massive headache too. Even with encryption, the sheer movement of massive datasets across unverified hardware creates leakage points. You can't simply lock a door when your compute power lives on someone else's machine in a different country.
Latency issues are equally stubborn. In a centralized setup, GPUs talk to each other via high-speed interconnects like NVLink. In a decentralized setup, you are at the mercy of standard internet protocols. The massive bandwidth required for gradient synchronization often chokes on residential or public connections. This communication overhead can make training orders of magnitude slower than traditional methods. You spend more time waiting for packets to arrive than actually performing matrix multiplications. This jitter makes scaling training jobs highly unpredictable.
