MPC-Optimized Transformer Components

• MPC-Optimized Transformer Components are tailored modifications to standard Transformer architectures that minimize MPC overhead for secure, privacy-preserving model inference.
• They leverage quantization, layer freezing, LoRA adaptation, and operator fusion to achieve significant runtime and communication improvements while maintaining accuracy.
• These optimizations integrate protocol-level enhancements and neural network reparameterizations to enable efficient, confidential deep learning in multi-party computation settings.

Multi-party computation (MPC)-optimized Transformer components are architectural and algorithmic modifications to Transformer models, their layers, and the surrounding protocol stack with the explicit goal of minimizing the cryptographic and communication overhead incurred during secure inference under MPC. These optimizations are critical in privacy-preserving LLM inference and secure collaborative machine learning, where either user prompts or model weights must remain confidential, but vanilla MPC is prohibitively expensive due to the high cost of operations such as multiplications and non-arithmetic primitives (e.g., comparisons, reciprocal, softmax) that arise pervasively in Transformer calculations. A series of recent works, including Marill, Ditto, BLB, and MPCFormer, have introduced distinct yet complementary strategies—encompassing quantization, operator fusion, layer freezing, architecture rewrites, and custom MPC protocols—to achieve large reductions in runtime and communication while preserving accuracy and strong cryptographic security guarantees.

1. Overheads in Standard Transformer Inference Under MPC

Traditional Transformer layers comprise multi-head self-attention (MHSA), position-wise feed-forward (FFN) sublayers, and layer normalization, each involving dense matrix multiplications and non-linear activations. Under MPC protocols, secure evaluation of these operations is bottlenecked by:

• Secret-shared multiplications ($O(bd_\text{model}^2)$ per linear or FFN layer, $b$ = batch size).
• Non-arithmetic operations, such as:
  • Softmax (exponentiation, division, max)
  • Comparisons (for activation or normalization)
  • Score normalization (reciprocal, root)
  • Each non-arithmetic gate—division, comparison, root—requires multiple communication rounds and may dominate protocol bandwidth (e.g., 79% of total communication in MPCFormer is spent in softmax and activation) (Li et al., 2022).

Consequently, naive private inference incurs severe slowdowns (60× or more), making direct secure deployment of LLMs impractical.

2. Quantization-Driven and Distillation-Based Optimizations

Quantization-aware strategies have shown effectiveness at reducing overhead by converting all computation to fixed-point, low-bitwidth formats:

• Static dyadic (fixed-point) quantization, layer-wise, with all scaling factors as $1/2^f$; bit-shifts instead of arbitrary scales (Wu et al., 9 May 2024). Linear/embedding layers use low-precision formats (e.g., $FXP_{32}^8$), while sensitive modules (softmax, layer norm) use higher precision (e.g., $FXP_{64}^{18}$).
• Quantized weights and activations allow all arithmetic to operate on smaller rings $\mathbb{Z}_{2^\ell}$, reducing the per-operation cost on shares and supporting efficient truncation (DownCast) and expansion (UpCast) protocols for type conversion, often requiring no interaction for dyadic shifts.

Loss of accuracy from quantization and the adoption of low-degree polynomial approximations for nonlinearities (e.g., GeLU replaced with $\mathrm{Quad}(x) = 0.125 x^2 + 0.25 x + 0.5$) is recovered through layer-wise knowledge distillation: the student model is initialized by quantization of the teacher weights, then trained to match layerwise and output activations of the teacher (Li et al., 2022, Wu et al., 9 May 2024).

This scheme achieves 3.14–4.40× speedup over MPCFormer and 1.44–2.35× over PUMA on BERT and GPT2, with under 1 point utility drop on GLUE/Perplexity tasks (Wu et al., 9 May 2024).

3. Layer Freezing, LoRA Adaptation, and Head Merging

Recent architectural reparameterizations designed for MPC workloads include:

• Layer Freezing: Only the top $fN$ layers of an $N$-layer Transformer are private/fine-tuned, allowing clients to evaluate the lower $(1-f)N$ layers entirely in the clear. Only the output from the last public layer (a public-weights function of the input) is secret-shared into MPC. This reduces end-to-end MPC cost by $1/f$ (e.g., $f=1/2$ yields $2\times$ improvement) (Rathee et al., 7 Aug 2024).
• LoRA Adaptation: Instead of private, dense linear weight matrices, only low-rank adaptation parameters $\Delta W = AB$ are secret-shared (where $A \in \mathbb{R}^{d_\text{model} \times r}$, $B \in \mathbb{R}^{r \times d_\text{model}}$, $r \ll d_\text{model}$), with the bulk of $W$ public. $XW$ can be computed outside MPC, and $XAB$ requires $O(2b d_\text{model} r)$ secret multiplies vs. $O(b d_\text{model}^2)$, a $25\times$ reduction at $d_\text{model} = 3200$, $r = 64$ (Rathee et al., 7 Aug 2024).
• Head Merging: In self-attention, multiple heads are merged into wide heads of dimension $m \cdot d$ (for merge size $m$). Non-arithmetic MPC cost in softmax/truncation scales with the number of heads $h$, so reducing $h \to h/m$ yields an $m\times$ reduction in these costs while keeping parameter count fixed. Grouping similar heads preserves accuracy significantly better than naive head pruning (Rathee et al., 7 Aug 2024).

Performance gains from combining these: 3.6–11.3× end-to-end runtime and 2.4–6.9× communication improvement across protocols, with $>$90% downstream accuracy maintained.

4. MPC-Friendly Nonlinear Approximations and Protocols

For MPC, high-communication or multi-round primitives such as softmax, GeLU, and LayerNorm are replaced by cost-effective alternatives:

• Quadratic and Piecewise Polynomial Approximations: GeLU via quadratic (Quad), softmax via 2Quad (square normalization) or BOLT/Bumblebee-style piecewise approximations. In Ditto and MPCFormer, softmax is replaced by $[(s_i + c)^2] / \sum_j (s_j + c)^2$ for a suitable $c$.
• Protocol-Level Optimization: MPCFormer uses Beaver triple secret-sharing for linear layers, and protocol-specific optimizations (e.g., mask-and-open, bitwise truncation) for type conversion in quantized models (Li et al., 2022, Wu et al., 9 May 2024).

Ablation studies indicate quantization and polynomial approximations alone provide 1.74–2.09× speedup over unoptimized baselines, while further communication savings accrue from upstream architectural rewrites (Wu et al., 9 May 2024).

5. Operator Fusion and Hybrid HE/MPC Protocols

Fine-grained operator decomposition and subsequent fusion enable entire chains of linear and expansion/reduction operations to be grouped into single computational units, eliminating expensive intermediate conversions:

• Operator Catalogs: BLB (Xu et al., 27 Aug 2025) splits transformations into identity (add/mul), expansion (broadcast), reduction (sum), and transformation (matmul). Operators are composed into DAGs representing entire Transformer blocks.
• Fusion Tables: Legal fusions are enumerated (e.g., back-to-back linear matmuls fused into a single matmul), with weight and bias fused algebraically ($W_\text{fused} = W_2 W_1$, $b_\text{fused} = W_2 b_1 + b_2$).

For settings combining homomorphic encryption (HE) and MPC (e.g., CKKS-MPC for BLB), secure conversion protocols (e.g., mask-and-decode from CKKS ciphertext to additive secret shares) efficiently transition data between the two modalities, hiding all structure and minimizing communication. BLB’s matrix multiplication protocol leverages multi-head packing and the baby-step giant-step method for rotation efficiency in HE (Xu et al., 27 Aug 2025).

BLB achieves 21× communication reduction vs. BOLT and 2× vs. Bumblebee on BERT and GPT2, with up to 13× latency reduction using GPU acceleration.

6. Security Guarantees and Empirical Results

All described optimizations are crafted to maintain the same simulation-based security guarantees as standard semi-honest (and, with extensions, malicious) MPC protocols. Public vs. private weight partitioning is deterministic; no additional leakage arises from LoRA or head-merge rewrites, as only newly private parameters remain secret-shared. For hybrid HE/MPC, conversion protocols provably mask all intermediate values.

Empirically, the most efficient frameworks report:

Protocol/Framework	Runtime Speedup	Comm. Reduction	Accuracy Drop
Marill (w/ LoRA, merge)	3.6–11.3×	2.4–6.9×	<10%
Ditto (Quad+Quant)	3.14–4.40×	1.28–1.70×	<1point
BLB	up to 21×	up to 23×	<0.2pp
MPCFormer (2Quad+KD)	2–6×	up to 79%↓	<3%

Functionality and performance persist across major Transformer backbones (BERT, GPT2, RoBERTa), sequence lengths, and under both LAN and WAN conditions (Rathee et al., 7 Aug 2024, Xu et al., 27 Aug 2025, Wu et al., 9 May 2024, Li et al., 2022).

7. Relation to MPC-Accelerated Model Predictive Control

A distinct but related thread is the use of Transformers as accelerators within model predictive control (MPC) solvers—e.g., TransMPC and TransformerMPC (Wu et al., 9 Sep 2025, Zinage et al., 14 Sep 2024). Here, Transformers are used not for privacy but to predict control policies or solver warm starts in real time, achieving reductions in computational load compared to classical QP solvers. These approaches are architecturally distinct from the MPC-secure Transformer inference problem but reflect the progressively tighter integration of Transformer design with structure awareness, operator fusion, and cryptographic constraint.

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References:

• "MPC-Minimized Secure LLM Inference" (Rathee et al., 7 Aug 2024)
• "Ditto: Quantization-aware Secure Inference of Transformers upon MPC" (Wu et al., 9 May 2024)
• "Breaking the Layer Barrier: Remodeling Private Transformer Inference with Hybrid CKKS and MPC" (Xu et al., 27 Aug 2025)
• "MPCFormer: fast, performant and private Transformer inference with MPC" (Li et al., 2022)
• "TransMPC: Transformer-based Explicit MPC with Variable Prediction Horizon" (Wu et al., 9 Sep 2025)
• "TransformerMPC: Accelerating Model Predictive Control via Transformers" (Zinage et al., 14 Sep 2024)