PrivLLMSwarm: Secure LLM Coordination

• PrivLLMSwarm is a privacy-preserving framework that enables distributed LLM training and encrypted inference using secure MPC, federated learning, and differential privacy.
• The system demonstrates effective real-time performance in UAV and IoT surveillance, achieving high accuracy and robust fairness through advanced aggregation and secure computation.
• It integrates MPC-optimized transformer layers and formal proofs via Isabelle, providing both practical implementations and theoretical guarantees for multi-agent data confidentiality.

PrivLLMSwarm is a privacy-preserving framework for the coordination and distributed learning of LLM swarms, designed to ensure data confidentiality and fairness in sensitive multi-agent systems such as UAV-based IoT surveillance and federated user-group alignment. It leverages secure computation paradigms—primarily Secure Multi-Party Computation (MPC), Federated Learning (FL), and Differential Privacy (DP)—to facilitate collaborative model training and encrypted LLM inference without compromising sensitive operational or user-group data. The architecture supports both formal privacy-theoretic guarantees and practical implementations, advancing the state-of-the-art in secure LLM-enabled swarm intelligence (Ayana et al., 7 Dec 2025, Srewa et al., 13 Mar 2025, Kammüller, 6 Dec 2025).

1. System Components and Architectural Paradigms

PrivLLMSwarm integrates the following key components depending on use case and deployment:

• Edge Clients/UAVs/User Devices: Each agent locally converts sensor data (e.g., images, text) into semantic features using pre-trained models (e.g., CLIP or Alpaca-7B embeddings), never exposing raw data.
• Encrypted Computation Parties: Data are secret-shared among at least three non-colluding parties (e.g., UAVs, operator stations, computation servers), enabling secure aggregation and inference via MPC. For Federated Learning, updates (not raw data) are transmitted to a central server using authenticated TLS channels.
• Transformer-Based Coordination Brain: A fine-tuned GPT-based transformer serves as both the command generator for autonomous UAV swarms and as a preference predictor for federated group alignment. Transformer layers, including self-attention and feedforward, are optimized for MPC, employing Beaver's triples for secure multiplication.
• Privacy-Enhancing Protocols: PrivLLMSwarm incorporates threshold homomorphic encryption, DP (via gradient clipping and calibrated Gaussian noise injection), and secure multi-level aggregation strategies.

The adversarial threat model is semi-honest: the system is robust against eavesdroppers and up to two colluding computation parties, ensuring that neither agents nor computation parties can reconstruct plaintext data from intermediate states (Ayana et al., 7 Dec 2025).

2. Privacy-Preserving Mechanisms in Distributed Swarms

PrivLLMSwarm achieves strong privacy guarantees through layered protocols:

• Federated Averaging (FedAvg): Each client trains a local preference predictor and shares weight updates $\Delta w_k$ encrypted via homomorphic schemes. Server-side aggregation proceeds:

$w^{t+1} = \sum_{k=1}^K \frac{n_k}{n} w_k^t$

preserving the influence of data-rich groups while keeping raw data local (Srewa et al., 13 Mar 2025).

• Differential Privacy: Before aggregation, each update is clipped to $\ell_2$ norm $C$ and additive Gaussian noise $N(0, \sigma^2 C^2 I)$ is injected, yielding $(\varepsilon, \delta)$-DP guarantees. Parameterized as:

$\sigma = C \sqrt{2 \ln(1.25/\delta)}/\varepsilon$

The privacy level $\varepsilon_t$ is tracked over multiple rounds via advanced composition (Kammüller, 6 Dec 2025).

• Secure MPC Inference: For encrypted LLM inference, each input token is secret-shared as $$\langle x \rangle_1, \langle x \rangle_2, \langle x \rangle_3$$ so that $$x = \langle x \rangle_1+\langle x \rangle_2+\langle x \rangle_3\ (\mathrm{mod}\ p)$$. Transformer computations use MPC-optimized linear and nonlinear approximations, including piecewise GELU and polynomial softmax implementations, minimizing communication rounds ($\approx$10–12/layer) and bandwidth (Ayana et al., 7 Dec 2025).

3. Learning Algorithms and Secure Model Update

• Local Training: Each edge client trains a transformer preference predictor on a local dataset $D_g = \{(x_i, y_i)\}$. The loss function is:

$$\mathcal{L}_g(\theta) = \mathbb{E}_{\text{contexts}} \left[ - \sum_{i=m+1}^{n} \log p_{\theta}(y_i | x_{1:m}, y_{1:m}, x_i) \right]$$

with regularization (weight decay, gradient clipping) and optional DP noise (Srewa et al., 13 Mar 2025).

• Encrypted Aggregation and Inference: MPC is leveraged to aggregate updates or conduct transformer inference across the swarm. Secret-shared matrix multiplication and polynomial approximations of GELU/softmax minimize computational and bandwidth overhead. Secure inference latency is practical for small to medium swarms—e.g., $417$ ms/image inference on a $4$-UAV system (Ayana et al., 7 Dec 2025).
• Group Fairness and Personalization: Alignment disparities are quantified using the Coefficient of Variation (CoV) and Fairness Index (FI). Adaptive weighting and client-side fine-tuning are supported to mitigate under-representation:

$$\text{CoV}(AS) = \frac{\sigma}{\mu}, \quad \text{FI}(AS) = \frac{1}{1+\text{CoV}(AS)^2}$$

A system with FI $\approx 1$ and low CoV is considered equitably aligned.

4. Formal Security Analysis and Attacker Models

PrivLLMSwarm's privacy guarantees are formalized in the Isabelle Insider and Infrastructure framework (IIIf):

• Differential Privacy for Federated Learning: Infrastructure states and transitions are defined so that for any two neighboring datasets $s_0, s_1$ and any measurable output set $S$,

$$\Pr_{t \leftarrow M.\text{paths to } s_0}[t \in S] \leq e^{\varepsilon} \Pr_{t \leftarrow M.\text{paths to } s_1}[t \in S] + \delta$$

(Kammüller, 6 Dec 2025)

• Attacker Advantage: For IIIf-modeled attackers,

$$\mathrm{Adv}(\mathcal{A}) = \Pr_{t \leftarrow \mathcal{D}_1}[\mathcal{A}(t)] - \Pr_{t \leftarrow \mathcal{D}_0}[\mathcal{A}(t)]$$

In the DP setting with $\delta=0$,

$$\mathrm{Adv}(\mathcal{A}) = 1 - \frac{1}{e^{\varepsilon}}$$

Demonstrably, as $\varepsilon \to 0$, advantage vanishes (Kammüller, 6 Dec 2025).

• Implementation Guidelines: Norm clipping ($C=1.0$), Gaussian mechanism calibration, client sampling, and Isabelle/Isar formal verification yield machine-checked DP for LLM swarms.

5. Experimental Performance and Comparative Analysis

PrivLLMSwarm has been experimentally validated in both UAV swarm (IoT surveillance) and federated group alignment contexts:

UAV Swarm Simulation (100×100 m urban airspace, 2–4 UAVs):

Swarm Size	Inference Compute (ms)	Communication (KB)	Accuracy (cos θ)	Formation Error (m)
2 UAVs	520.5	864	0.90	1.2
3 UAVs	780.8	1286	0.90	—
4 UAVs	1041.1	1726	0.90	0.8

• Encrypted Inference Latency: $417.69$ ms/image, $15.42$ ms/text command.
• Reliability: 94% “suitable” or “highly suitable” commands in human evaluation, 92% obstacle avoidance success, zero collisions in 50 test missions.
• Privacy-Utility Tradeoff: Compared to DP and FL baselines, PrivLLMSwarm achieves higher privacy (2-out-of-3 security), superior utility (only $5\%$ drop vs. plaintext), and moderate computational cost (Ayana et al., 7 Dec 2025).

Federated Preference Alignment:

• Convergence Speed: 46% reduction in training iterations ($634$ rounds vs. $1180$ epochs).
• Alignment Improvement: 4% higher mean alignment score (lower Jensen-Shannon distance) on held-out groups.
• Fairness: FI $\approx 1$, CoV consistently lower than centralized baselines, indicating near-perfect parity (Srewa et al., 13 Mar 2025).

6. Future Directions and Limitations

PrivLLMSwarm faces several open challenges:

• Scalability: Linear MPC scaling limits the size of feasible swarms; hierarchical or federated MPC could address this for $>10$–$15$ nodes.
• Connectivity: Reliable, low-latency communication channels are necessary for both MPC secure inference and FL aggregation.
• Adversarial Robustness: Current deployment assumes semi-honest adversaries; extension to malicious adversary models (e.g., Byzantine-secure MPC) is needed for critical infrastructures.
• Generalization: Transitioning from Q/A classification and discrete prompt-based security to open-ended generation/summarization remains an active area of research (Duan et al., 2023).
• Formal Verification: Isabelle/IIIf formalism provides rigorous proofs of $(\varepsilon, \delta)$-DP and attacker advantage, but implementation in real-world heterogeneous edge networks may present new challenges (Kammüller, 6 Dec 2025).

A plausible implication is that PrivLLMSwarm provides a reproducible blueprint for privacy-preserving, fair, and scalable distributed LLM systems, combining the strongest available protocols in secure computation, formal verification, and empirical performance analysis.

7. Practical Implications and Deployment

PrivLLMSwarm is publicly released with source code, MPC primitives (CrypTen/Secretflow), and a synthetic UAV IoT dataset for reproducibility (Ayana et al., 7 Dec 2025). Key deployment recommendations include:

• DP Calibration: Use $C=1.0$, $\epsilon=8$, $\delta=10^{-6}$, yielding $\sigma \approx 1.2$ for Gaussian mechanism.
• Personalization and Multi-Level Aggregation: Support hierarchical Federation (device→edge→cloud), adaptive group re-weighting, and on-device reward evaluation for RLHF.
• Prompt Privacy: Private prompt learning is feasible with PromptDPSGD and PromptPATE, suitable for tasks where API access limits gradient-level privacy enforcement (Duan et al., 2023).
• Isabelle Formal Verification: Formal model encoding and theorem proving are recommended for deployment-critical privacy guarantees in federated environments (Kammüller, 6 Dec 2025).

PrivLLMSwarm defines the state-of-the-art along the intersection of privacy, distributed LLM learning, and secure autonomous swarm coordination, supporting real-time, confidential operation in sensitive application domains and providing robust theoretical underpinnings for DP and MPC-based LLM systems.