# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
## 3 Proposed Methods: The SecureLLM Framework
### Layer 2: Decentralized Fine-Tuning  
We employ a decentralized model training protocol inspired by federated learning but optimized for LLMs. SecureLLM uses an MPC-based aggregator that collects encrypted gradient updates from multiple clients and aggregates them without revealing the individual contributions. It is important to note that our aggregator is implemented as a multi-node MPC system. This design not only distributes the computation but also ensures that no single node can decrypt the individual gradients. Furthermore, digital signatures and robust anomaly detection (e.g., threshold-based rejection) are integrated to validate each update and to mitigate risks from malicious actors such as Sybil or backdoor attacks. This design helps organizations collaborate on LLM development without exposing proprietary or sensitive data. By encrypting every gradient end to end, SecureLLM eliminates the risk of partial updates being eavesdropped on, which can reveal sensitive data patterns. Additionally, the aggregator itself operates in a cryptographically hardened environment, allowing global model updates to be combined securely even when the participants may not fully trust each other [38]. To handle the large parameter sizes typical of LLMs (often billions of parameters), SecureLLM incorporates gradient compression techniques such as top-\(k\) sparsification or quantization to reduce the communication overhead [39]. An adaptive scheduling algorithm determines the optimal intervals for gradient exchange, taking into account the convergence rates, fine-tuning objectives, and participants' computational resources [40]. This \"adaptive scheduling algorithm\" continuously monitors the model states (e.g., the gradient variance and loss plateau signals) to dynamically adjust the frequency and volume of gradient sharing, minimizing performance degradation under cryptographic constraints. Within this decentralized paradigm, each participant retains control over its local data while contributing only encrypted gradients to the global model state. This setup is particularly appealing for multi-stakeholder consortia--such as hospitals, financial institutions, or government agencies--where data-sharing agreements can be fraught with privacy or compliance issues. The aggregator in MPC adds a layer of security by preventing the central server from decrypting updates, thus keeping each participant's information private even in semi-trusted or partially compromised environments. Another significant improvement is how SecureLLM integrates compression techniques into the encryption mechanism. Rather than encrypting raw gradients directly, the system first applies methods such as top-\(k\) sparsification or quantization, thus reducing the amount of information that must be encrypted. By decoupling compression and encryption, participants can tailor the compression strategies based on their unique datasets, potentially improving the overall model convergence while maintaining robust security. In addition to standard decentralized fine-tuning, SecureLLM addresses security threats like Sybil attacks and malicious gradient injection. We propose integrating digital signatures for each gradient update, anomaly detection to flag irregular update patterns, and consensus protocols among nodes to validate gradient integrity. These measures ensure that only legitimate contributions are incorporated into model updates, enhancing the resilience of the decentralized training process. **Algorithm 1: Secure Decentralized Gradient Aggregation. **  
Let \(g_{i}\) be the gradient update from participant \(i\) and \(\sigma(g_{i})\) its digital signature. The aggregator verifies each \(\sigma(g_{i})\) and computes the aggregated gradient as  
\[G=\frac{1}{N}\sum_{i=1}^{N}g_{i}, \tag{1}\]  
subject to the constraint that each \(g_{i}\) passes an anomaly detection test \(\mathcal{A}(g_{i})\). A consensus mechanism is then applied such that only gradients satisfying \(\mathcal{C}(g_{i})=\text{true}\) are used.