Research on English Learning Content Rendering and Interactive Application Based on Multimedia Technology

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Comparison of LeNet-5 and WN-LeNet-5 Neural Network Accuracy

Algorithm	Privacy Level(ε)
	7	3	1	0.5	0.1
DPSGD	93.12%	92.65%	91.15%	90.05%	83.10
WN-DPSGD	94.63%	93.06%	92.77%	91.63%	88.68

Privacy loss boundary value of different combination mechanisms

Method	Privacy budget boundary value
Common combination mechanism	(O(qTε),qTδ)–DP
Strong combination mechanism	(O(qεTlog1/δ),qTδ)−DP(O(q\varepsilon \sqrt{T\log 1/\delta }),qT\delta )-DP
Moment accountant mechanism	(O(qεT),δ)−DP(O(q\varepsilon \sqrt{T}),\delta )-DP

Comparison of parameters between ResNet-WN-18 and 5ResNet-18

Layer name	ResNet-18	Res Net-WN-18
Convolution	1728	1728
Normalization layer	128	0
Layer 1	147968	147456
Layer 2	517120	516096
Layer 3	2066432	2064384
Layer 4	8261632	8257536
Linear	5130	5130
Total	11000138	10992330

Comparison of neural network accuracy with different weight noise levels

Model	Weighted noise level
	0	0.001	0.1	1	2
LeNet-5	99.20%	98.72%	98.01%	Nonconvergence	Nonconvergence
BN-LeNet-5	99.20%	99.17%	99.17%	99.14%	99.08%
WN-LeNet-5	99.16%	99.15%	99.15%	99.12%	99.07%

R Default Parameters

Parameter	Default
η	0.9
θ	0.01
ζ	10–6
τ	5000
∈	0.1
Q	100
R	10
L	288

Experimental Simulation Environment

Name	Version model
GPU	GeForce GTX 1650 (8GB)
GPU	Intel Core i7
Python	3.8.5
Pytorch	1.71 (GPU version)

Effect of hyperbolic discount factor on average relative error

θ	∈= 0.01	∈= 0.03	∈= 0.05	∈= 0.07	∈= 0.09
0.01	9.5906	3.1894	3.1364	1.6202	1.3371
0.1	4.0158	2.2728	1.2470	1.0315	0.8132
1	2.9822	1.3099	0.8551	0.5707	0.3469
10	0.9605	0.2263	0.2120	0.2106	0.1672
100	0.2730	0.1590	0.1488	0.1346	0.1347

Floating point Calculation Times Statistics of Batch Normalization Operation

Batch normalization operation	Floating point number of operations
μj=1m∑​i=1mZj(i){{\mu }_{j}}=\frac{1}{m}\underset{m}{\overset{i=1}{\mathop{{{\mathop{\sum }^{}}^{}}}}}\,Z_{j}^{(i)}	Hl–1 × Wl–1 × (B – 1) Secondary addition
σj2=1m∑​i=1m(Zj(i)−μj)2\sigma _{j}^{2}=\frac{1}{m}\underset{m}{\overset{i=1}{\mathop{{{\mathop{\sum }^{}}^{}}}}}\,{{(Z_{j}^{(i)}-{{\mu }_{j}})}^{2}}	(B×Hl−1×Wl−1)+Hl−1×Wl−1×(B−1) Subaddition B×Hl−1×Wl−1+1 Sub multiplication$\begin{matrix} (B\times {{H}_{l-1}}\times {{W}_{l-1}})+{{H}_{l-1}}\times {{W}_{l-1}}\times (B-1){{\quad }_{~\text{Subaddition }\!\!~\!\!\text{ }}} \\ B\times {{H}_{l-1}}\times {{W}_{l-1}}+1\quad {{~}_{\text{Sub }\!\!~\!\!\text{ multiplication}}}~ \\ \end{matrix}$
γkXk−μkIσk2+ε+βkI{{\gamma }_{k}}\frac{{{X}_{k}}-{{\mu }_{k}}I}{\sqrt{\sigma _{k}^{2}+\varepsilon }}+{{\beta }_{k}}I	Hl−1×Wl−1 Subaddition Hl−1×Wl−1 Sub multiplication\begin{matrix} {{H}_{l-1}}\times {{W}_{l-1}}{{~}_{\text{Subaddition}}}~ \\ {{H}_{l-1}}\times {{W}_{l-1}}\quad {{~}_{\text{Sub }\!\!~\!\!\text{ multiplication}}}~ \\ \end{matrix}

Comparison of algorithm privacy loss

Data set	Accuracy	δ	Loss of privacyDPSGD	Loss of privacyDpAdam	Loss of privacyDPAdaMod
MNIST	88.00%	10–5	0.71	0.615	0.56
	90.00%		1.28	1.09	0.921
	92.00%		1.78	1.32	1.23
	94.00%		5.73	3.68	2.98