Federated Learning Nodes Can Reconstruct Peers' Image Data

## Abstract

Abstract Federated learning (FL) is a privacy-preserving machine learning framework that enables multiple nodes to train models on their local data and periodically average weight updates to benefit from other nodes’ training. Each node’s goal is to collaborate with other nodes to improve the model’s performance while keeping its training data private. However, this framework does not guarantee data privacy. Prior work has shown that the gradient-sharing steps in FL can be vulnerable to data reconstruction attacks from an honest-but-curious central server. In this work, we show that an honest-but-curious node/client can also launch attacks to reconstruct peers’ image data in a centralized system, presenting a severe privacy risk. We demonstrate that a single client can silently reconstruct other clients’ private images using diluted information available within consecutive updates. We leverage state-of-the-art diffusion models to enhance the perceptual quality and recognizability of the reconstructed images, further demonstrating the risk of information leakage at a semantic level. This highlights the need for more robust privacy-preserving mechanisms that protect against silent client-side attacks during federated training.

## 1 Introduction

Federated learning (FL) has attracted significant attention as a promising approach to privacy-preserving machine learning (McMahan et al., ; Kairouz et al., ). In this framework, a central server coordinates training by multiple clients. In each training round, the server broadcasts a shared model to a subset of clients. Each client computes a gradient by training the model on its private data and returns the gradient to the server. The server then averages all the gradients and updates the model. This approach enables each participant to benefit from a model trained on more data without sharing its own data. FL has the potential to revolutionize collaborative efforts in such real-world applications as healthcare and finance, enabling participants to train better models without compromising data privacy (Li et al., ).

Despite the intent to protect privacy through FL, prior works have shown that an honest-but-curious central server can reconstruct a client’s training data. This is done by adjusting a dummy input to the model until its resulting gradient closely matches the gradient sent by the client (Zhu et al., ; Geiping et al., ; Yue et al., ). Meanwhile, studies on malicious clients have shown that a client can disrupt federated training by sending adversarial data to the server (Blanchard et al., ; Shi et al., ). However, far less attention has been given to the potential for clients to reconstruct others’ data while honestly participating in the FL network.

Figure: Figure 1: Overview of the proposed honest-but-curious client attack. The attacker participates in two consecutive training rounds to obtain the global model’s gradient update by ① differencing the model weights. The attacker then ② inverts the update to obtain each client’s training data. To address the challenge of recovering high-quality images from diluted information hidden in the global gradient update, the raw reconstructions are postprocessed using either ③ a direct technique respecting pixel-level correctness or ④ a semantic technique focusing on producing photorealistic images.

Our work explores the extent to which a single client can reconstruct peers’ training data while adhering to FL protocols. We introduce a novel client-to-client attack depicted in Figure 1, where the attacker exploits weight updates between consecutive training rounds to reconstruct training images. By participating in two consecutive training rounds and comparing the global model’s weights, a single client extracts the averaged gradient of all participants in the earlier round. Unlike server-side gradient inversion (Zhu et al., ; Geiping et al., ; Yue et al., ), this attack requires isolating individual data from a diluted mixture of gradient updates. Despite this challenge, we show that the attacker is able to reconstruct images from every other client.

We further propose to utilize two image postprocessing techniques based on diffusion models to improve the quality and recognizability of attack’s raw reconstructed images. Our first technique uses a pretrained masked diffusion transformer (MDT) (Gao et al., ) to generate high quality images that resemble the raw attack results on a semantic level. Our second technique uses denoising diffusion probabilistic models (DDPMs) (Ho et al., ) to enhance the raw reconstructions at the pixel level through super resolution and denoising (Kawar et al., ). This paper’s contributions are threefold.

- 1.
We demonstrate that clients participating in FedAvg (McMahan et al., ) can exploit the model updating process to reconstruct peers’ data, which reveals a previously unrecognized privacy risk in FL.
- 2.
The proposed masked diffusion enhancer (MDE) generates sharp, high-resolution images from the low-resolution, color-aliased raw reconstructions. The generated images resemble a target image on a semantic level, preserving its geometric shape and perceptual features with photorealistic quality.
- 3.
The proposed DDPM-based image postprocessing simultaneously denoises and upsamples raw reconstructed images. This improves image resolution and object recognizability, achieving strong pixel-wise similarity to ground-truth images.

## 2 Related Work

Server-Side Gradient Inversion.
The assumption that FL inherently protects data privacy has been challenged by studies exposing vulnerabilities to gradient inversion attacks from the central server (Zhu et al., ; Geiping et al., ). These attacks exploit the gradients shared by clients to reconstruct private training data. They revealed that by iteratively updating a dummy input to produce a gradient similar to a given target gradient, the server could generate images closely resembling the participant’s original training data. Various defense mechanisms have been proposed to protect against these attacks, including gradient compression, perturbation, and differential privacy techniques (Zhang et al., ; Sun et al., ). Despite these efforts, recent studies have shown that these defenses may not effectively prevent training data from being meaningfully reconstructed. For example, Yue et al. () overcame these defense methods by leveraging latent space reconstruction and incorporating generative models to remove distortion from reconstructed images.

Client-Side Model Inversion.
While the majority of research has focused on server-side attacks, the potential for client-side attacks has been less examined. Wu et al. () investigated model inversion attacks, where a client exploits the model’s overfitting to reconstruct training data. This approach relies on manipulating the model rather than directly reconstructing other clients’ data. Similar to our work’s takeaway, their results demonstrated that clients can reconstruct peers’ images without disrupting the training process. However, due to the challenging nature of model inversion, their method produces reconstructed images far less similar to the target than those from gradient inversion attacks.

Malicious Client Attacks.
A parallel research direction focuses on attacks where a malicious client interferes with the FL process (Blanchard et al., ; Shejwalkar et al., ). Specifically, malicious clients can manipulate the model updates by using poisoned data or sending poisoned gradients to the server to impede convergence. Meanwhile, researchers have shown that malicious modifications can compromise privacy easily (Fowl et al., ; Wen et al., ).
While this introduces unique security challenges in FL, our attack does not disrupt the training process and is difficult to be detected by the server or other clients.

## 3 Proposed Image Reconstruction Attack by Curious Clients

In this section, we present the gradient inversion attack, which allows an honest-but-curious client to reconstruct image data from other clients.
To enhance this reconstruction, two postprocessing methods are introduced to achieve both fine-grained quality and perceptual realism.
The first postprocessing method improves the images at the pixel level with enhanced details. The second method, built on a masked diffusion enhancer, refines the images at the semantic level and produces photorealistic reconstructions.

### 3.1 Attack Framework

Threat Model. We consider an honest-but-curious client (or curious client, for simplicity) attacker.
It aims to reconstruct other clients’ training data while following the protocol of FL. The attacker does not disrupt the model training process. The curious client does not have direct access to the gradients from other clients. However, it receives an updated version of the shared model from the server at each communication round. We assume the client has sufficient computational resources to perform the optimization process during its participation in FL. Additionally, we assume as in (Li et al., ; Huang et al., ) that all client updates in a given round have been computed using the same learning rate, which is applied locally if each client transmits a model update, as shown in Eq. (1). It may also be applied globally if clients transmit raw gradients to the server, as discussed in Section 4. Each client uses the same number of images for local training and the attacker knows or can guess the number of clients in each training round.
We follow the assumption of Yue et al. () that each client performs full batch gradient descent during its local training and that the class labels have been analytically inverted as in Yin et al. ().
We target cross-silo FL scenarios, in which a small number of clients collaborate to overcome data scarcity. For example, a group of hospitals may use FL to develop a classifier for rare diseases from CT scans, where each has limited training examples and images cannot be directly shared due to privacy concerns.

Our approach to reconstructing data from the global model updates builds upon traditional gradient inversion and includes extra initialization and calculation steps to separate individual training images from the averaged global update. Our attacker engages in two consecutive rounds of FedAvg and obtains two versions of the global model parameters, $\mathbf{W}^{(t)}$ and $\mathbf{W}^{(t+1)}$. By computing the difference between successive model weights, the attacker can infer the gradient used for the global model update: $\mathbf{\nabla}_{\text{avg}}^{(t)}=(\mathbf{W}^{(t)}-\mathbf{W}^{(t+1)})/\eta_
{\text{g}}^{(t)}$, where $\eta_{\text{g}}^{(t)}$ is the globally-determined learning rate for round $t$.

To separately reconstruct each image, our attacker initializes $\mathbf{X}\in\mathbb{R}^{N\times C\times H\times W}$, where $N=K\times M$ represents the total number of training examples across $K$ clients with batch size $M$. $C$, $H$, and $W$ are the number of channels, and the height and width of the target images, respectively. We note that averaging the weight updates of several clients performing full-batch gradient descent is mathematically equivalent to computing a weight update over all clients’ training images. For $K$ clients, each computing a local update $\mathbf{W}_{k}$ using full-batch gradient descent over a local dataset of size $M$, the server updates the global model’s weights by averaging the weights transmitted by each client: $\mathbf{W}^{(t+1)}=\frac{1}{K}\sum_{k=1}^{K}\mathbf{W}_{k}^{(t+1)}$. For each client using full batch gradient descent, the local weight update can be expressed as:

$$ $\mathbf{W}_{k}^{(t+1)}=\mathbf{W}^{(t)}-\eta_{\text{g}}\frac{1}{M}\sum_{i=1}^{ M}f(\mathbf{W}^{(t)};\mathbf{X}_{k,i}),$ (1) $$

where $\mathbf{X}_{k,i}\in\mathbb{R}^{C\times H\times W}$ is the $i$th input from client $k$ and $\mathbf{W}^{(t)}$ represents the model parameters at time $t$. Substituting $\mathbf{W}_{k}^{(t+1)}$ into the expression for $\mathbf{W}^{(t+1)}$, we obtain:

$$ $\mathbf{W}^{(t+1)}=\frac{1}{K}\sum_{k=1}^{K}\left(\mathbf{W}^{(t)}-\eta_{\text {g}}\frac{1}{M}\sum_{i=1}^{M}f(\mathbf{W}^{(t)};\mathbf{X}_{k,i})\right)= \mathbf{W}^{(t)}-\eta_{\text{g}}\frac{1}{N}\sum_{i=1}^{N}f(\mathbf{W}^{(t)}; \mathbf{X}_{i}),$ (2) $$

where $X_{i}$ are the inputs from the combined dataset of all clients.

The attacker passes $\mathbf{X}$ through the global model and compares the resulting gradient update $\nabla_{\mathbf{X}}^{(t)}\equiv f(\mathbf{W}^{(t)};\mathbf{X})$ to the target gradient $\nabla_{\text{avg}}^{(t)}$. Following the gradient inversion framework, the goal is to iteratively refine $\mathbf{X}$ until it closely approximates the data used to compute the target gradient. The attacker solves the following optimization problem.

$$ $\hat{\mathbf{X}}=\arg\min_{\mathbf{X}}\left\|f(\mathbf{W}^{(t)};\mathbf{X})- \nabla_{\text{avg}}^{(t)}\right\|^{2}.$ (3) $$

Finally, the attacker applies a postprocessing function $\phi(\cdot)$ to improve the quality of the reconstructed images $\tilde{\mathbf{X}}=\phi(\hat{\mathbf{X}})$. This shows that a curious client attacker is able to follow an approach similar to server-side gradient inversion and obtain reconstructed data from all other clients from only two consecutive versions of the model weights. We describe below two methods of postprocessing $\hat{\mathbf{X}}$ to improve its quality at either a pixel or semantic level.

### 3.2 Direct Postprocessing for Pixel-Level Image Enhancement

To reconstruct the target data more effectively, we introduce a direct postprocessing method that utilizes pretrained diffusion models to perform super resolution and denoising on the raw image reconstructions. The raw reconstructed images from the attack may be low-resolution or have pixel artifacts due to imperfect gradient inversion. This problem may also be more severe in our attack compared to server-side gradient inversion as the target gradient contains diluted information from multiple clients. To address this problem, we introduce a postprocessing implementation, $\phi(\cdot)\equiv\phi_{\text{direct}}(\cdot)$ that uses pretrained diffusion models to directly postprocess the raw reconstructed images.
Diffusion models have demonstrated good performance in image generation and restoration tasks and are able to produce more realistic images with a lower likelihood of hallucination (Dhariwal & Nichol, ). Our method follows the denoising diffusion restoration models (DDRM) framework and utilizes a pretrained DDPM (Ho et al., ) as a backbone model. DDRM has demonstrated strong performance across various image restoration tasks, including super resolution and denoising (Kawar et al., ). By increasing resolution and removing noise, we aim to accurately reveal details of the ground truth images and make the reconstructions more recognizable.

### 3.3 Masked Diffusion Enhancer: reconstruction at a semantic level

In this subsection, we also introduce a method to reconstruct target images at the semantic level, $\phi(\cdot)\equiv\phi_{\text{semantic}}(\cdot)$, the masked diffusion enhancer (MDE). The goal of MDE is to generate sharp, high-resolution images from the low-resolution, color-aliased raw attack results. This approach complements the direct postprocessing technique, as the generated images resemble the raw reconstructions at the semantic level, rather than at the pixel level.
The generated images preserve the shape and perceptual features of the target image while achieving photorealistic quality.

Backbone Model. We use masked diffusion transformer (MDT) as the backbone of our reconstruction technique. MDT has been proven to achieve state-of-the-art performance in image generation (Gao et al., ). Due to its extensive training and flexibility, MDT has learned a complex representation of each image class that enables it to accurately reconstruct each image’s semantic features through projection onto the manifold. Following the diffusion framework, MDT generates images by starting from a Gaussian noise vector $\mathbf{X}_{T}\sim\mathcal{N}(\textbf{\text{0}},\textbf{\text{I}})$, where $T$ is the total number of diffusion steps. At each step $t$, the model predicts a noise residual $\epsilon_{\theta}(\mathbf{X}_{t})$, and uses it to refine the noisy image $\mathbf{X}_{t}$ to $\mathbf{X}_{t-1}$. After $T$ iterations, the initial noise vector $\mathbf{X}_{T}$ will be transformed into a high quality image $\mathbf{X}_{\text{0}}$. For our reconstruction technique, we leverage a pretrained MDT and freeze its model parameters throughout the process to maintain consistency in the image generation pipeline.

Proposed Masked Diffusion Enhancer (MDE). MDE optimizes the initial noise vector $\mathbf{X}_{T}$ to generate an image that closely matches a target image $\hat{\mathbf{X}}$. During optimization, the noise predictions $\epsilon_{\theta}(\mathbf{X}_{t})$ at each timestep are treated as constants. The objective of MDE is to minimize the mean squared error (MSE) between the final generated image $\mu_{\theta}(X_{T},T)$ and the target image $\hat{\mathbf{X}}$:

$$ $\tilde{\mathbf{X}}_{T}=\arg\min_{\mathbf{X}_{T}}\left\|\mu_{\theta}(\mathbf{X} _{T},T)-\hat{\mathbf{X}}\right\|_{2}^{2},$ (4) $$

where $\mu_{\theta}(\mathbf{X}_{T},T)$ denotes the final image produced from the initial noise vector $\mathbf{X}_{T}$ after all diffusion steps.
By optimizing $\mathbf{X}_{T}$ based on the loss term, we guide the model to generate images that have the same shape and perceptual features as the target image.

Figure: Figure 2: Photorealistic images reconstructed by the proposed semantic reconstruction method MDE. This diffusion-based method iteratively refines its generated image by referring to the raw reconstruction. As iterations progress, the image increasingly assumes the shape and perceptual features of the raw reconstruction. After some optimal epoch number (determined by visual inspection of the attacker), the reconstructed image strongly resembles the target at a high quality. Beyond this point, further optimization may produce blurry images due to overfitting.
Refer to caption: x1.png

## 4 Experimental Results

This section first presents the performance of the proposed reconstruction attack in terms of image reconstruction quality against gradients averaged from multiple clients.
Factors affecting reconstruction quality, including the number of local epochs and client batch size, will be analyzed.
The postprocessing modules will be ablated to examine their benefits on image reconstruction.
The state of the art will be compared and the limitation of the proposed attack will be discussed.

Experimental Conditions. We evaluate our reconstruction attacks using the ImageNet (Deng et al., ) and MNIST (LeCun et al., ) datasets. We employ LeNet (LeCun et al., ) and ResNet (He et al., ) as the global models and conduct experiments under the FedAvg framework. Each client performs local training on 16 images as this batch size provides a baseline where the attack reconstructs recognizable images from the target gradient.
As more clients participate in training, the training image count from the global model’s perspective increases proportionally.
Before inverting the target gradient, the attacker encodes its dummy data through bicubic sampling to reduce the number of unknown parameters. This has been proven to save convergence time and improve image quality in gradient inversion (Yue et al., ).

Main Results. Figure 2 demonstrates that MDE effectively transforms low-resolution, color-aliased raw reconstructions into sharp, high-resolution images. The model was provided with randomly selected reconstructions from an attack on a system with four clients and iteratively refined the outputs over 100 epochs. In the first epoch, the output is a random image from the target class. As optimization progresses, the generated images increasingly resemble the target. We observe that at an optimal epoch number, the output images closely match the target, preserving its geometric structure and perceptual features with photorealistic quality. This optimal point varies across target images and was determined qualitatively. Beyond this point, although the generated images continue to match the target semantically, their quality degrades, becoming blurrier. We attribute this to the model overfitting that learns the pixelation and blurriness of the target to minimize the MSE loss.

Figure: Figure 4: LPIPS of reconstructed images vs. number of clients with three different models: LeNet5, ResNet9, and ResNet18.
Refer to caption: x2.png

Figure: Figure 3: Pixel-level correct images reconstructed by the proposed attack before and after direct postprocessing (Section 3.2). The second and third rows show postprocessed and raw reconstructed images. The raw reconstruction results from our attack are low-resolution and have significant color aliasing. Our direct postprocessing method increases the resolution and simultaneously denoises the images while maintaining pixel-level correctness, revealing image details that make the reconstructions easier to recognize.
Refer to caption: x3.png

Figure 3 illustrates the impact of the proposed DDPM-based direct image postprocessing in enhancing the quality of the raw reconstructions while preserving pixel-wise accuracy. The raw reconstructed images, constrained by the dummy data’s encoding, are 32$\times$32 pixels compared to the 128$\times$128 ground-truth images and exhibit pixel artifacts and color aliasing due to imperfect reconstruction. Our direct postprocessing method simultaneously performs super-resolution and denoising, addressing these quality issues. The resulting images are sharper, more recognizable, and retain details that closely match the ground truth, significantly improving resolution and object recognizability over the raw reconstructions.

Figure: Figure 5: Pixel-level correct images reconstructed from the proposed honest-but-curious client-based attack. Rows 2–4 show reconstructions from gradients averaged across 2, 4, and 8 clients with 16 images per client and 3 local epochs. The images remain high quality even when the attack is performed against multiple clients.
Refer to caption: x4.png

Figure 5 shows reconstructed images from attacks against systems with 2, 4, and 8 clients. As the number of clients increases, the reconstruction task becomes more difficult but we observe that our attack is still able to effectively reconstruct images from the target gradient. However, we observe a gradual decline in image reconstruction quality, measured by LPIPS (Zhang et al., ) and SSIM (Wang et al., ) of the reconstructed images, as shown in Figure 4.
With a larger number of clients, the initial reconstructions exhibit increasing levels of noise and color aliasing. This trend is consistent across a range of global models because the information contained within the target gradient becomes increasingly diluted as it is averaged from more clients.

Figure: Figure 6: LPIPS of reconstructed images vs. number of clients, client batch size, and local epochs. Reconstruction quality worsens with more clients and larger batch sizes.
Refer to caption: x5.png

Factor Analysis. Figure 6 reveals the effect of local epochs and client batch size on image reconstruction quality. These factors directly influence the attack’s ability to invert the target gradient.
As shown in the left plot of Figure 6, larger client batch sizes lead to worse reconstruction quality. Smaller batches add variability to updates, making them more informative for the attacker, whereas larger batches smooth updates and reduce the amount of exploitable information. The right plot of Figure 6 shows that increasing the number of local epochs leads to worse reconstructions when the number of clients is large. More local epochs cause greater gradient averaging, which dilutes the information needed to accurately reconstruct images from the target gradient. This is particularly important because federated learning often uses more local epochs to reduce communication.

Figure: Figure 7: LPIPS of reconstructed images with varying number of clients. Our direct postprocessing technique significantly improves reconstruction quality compared to the raw attack results.
Refer to caption: x6.png

Ablation Study. We examine how much impact the direct and semantic postprocessing blocks have on the quality of the attacker’s reconstructed images.
Figure 7 shows that directly postprocessing the raw reconstructed images results in a 20–30% improvement in LPIPS for systems with 2–8 clients. Figure 3 visually compares reconstructed images from a system with four clients before and after direct postprocessing. The raw images are low resolution and may be somewhat difficult to recognize while the postprocessed versions show much finer details and have recognizable features. This demonstrates the utility of our direct postprocessing technique in increasing the pixel-wise accuracy and recognizability of the reconstructed images.

Additionally, Figure 2 shows the effect of postprocessing the raw reconstructed images using the proposed MDE. The final results have the same shape and perceptual features of the raw reconstructions without any pixel artifacts, color aliasing, or blurriness. However, MDE’s goal is not to achieve pixel-wise accuracy so the generated images should not be compared quantitatively to the raw reconstructions.

Figure: Figure 8: Pixel-level-correct reconstructed images from a system with 16 clients. With more clients, our reconstruction technique can reconstruct only a subset of training images with high quality, whereas others show distortion and color aliasing artifacts.
Refer to caption: x7.png

Limitations. Our attack struggles to reconstruct high-quality images in systems where the number of clients is large. As the information contained within the attacker’s target gradient is averaged from more clients, it becomes more difficult to reconstruct high-quality images. With more than 8 clients, we observe that some reconstructed images remain high quality while others exhibit significant degradation or are not recognizable. Figure 8 shows the varying quality in our reconstructed images in a system of 16 clients.

Figure: Figure 9: Quality of the raw reconstructed images when the attacker incorrectly guesses the global model’s learning rate (LR). Image quality improves as the attacker guesses more correctly.
Refer to caption: x8.png

Unknown Learning Rate.
We assume in our experiments that the attacker knows the global learning rate $\eta_{\text{g}}$. This assumption simplifies the attack but need not be true for the attack to be effective. If the learning rate is applied globally (by the central server) and the attacker’s guess differs from the true value, the target gradient will be inversely scaled by a factor of the ratio between the guessed learning rate and the true learning rate,
leading to poor reconstruction quality. For simplicity, we set the base $\eta_{\text{g}}=1$ and examine the impact on reconstruction when it is unknown to the attacker. Figure 9 shows that reconstruction quality degrades rapidly as the guessed and true learning rates diverge. However, within an order of magnitude of the true learning rate, the degradation follows a simple polynomial pattern. An attacker with sufficient computational resources can refine their guess over multiple iterations to improve reconstruction quality if the true learning rate is not known.

Comparison to ROG and FedInverse. We compare our attack method to FedInverse (Wu et al., ) and reconstruction from obfuscated gradients (ROG) (Yue et al., ). To the best of our knowledge, FedInverse is the only prior work addressing honest-but-curious client attacks. Unlike our approach, which reconstructs data from gradients, FedInverse inverts the global model. Their method performs best when the model is complex and trained over many epochs, whereas our attack works best with larger gradients, typically when the model is simpler and less trained. Figure 12 reveals that our method reconstructs higher-quality data from a small number of clients, whereas FedInverse produced lower-quality reconstructions from a larger number of clients. FedInverse is less sensitive to the number of clients, while our approach is more sensitive. Our proposed attack can be viewed as complementary to FedInverse.

Figure: Figure 10: LPIPS and SSIM of reconstructed images using our direct postprocessor vs. GAN postprocessing with varying number of clients. Our method results in higher LPIPS as it blurs uncertain image details, compared to sharper but potentially less accurate outputs from GANs. SSIM, less sensitive to blurring, remains comparable across both approaches.
Refer to caption: x9.png

We compare reconstruction quality from our direct and semantic postprocessing techniques to the state-of-the-art postprocessing results achieved by Yue et al. (). Figure 11 shows that our technique generates images that are more recognizable but often blurry because of uncertainty in the fine image details. LPIPS is designed to evaluate image quality, rather than detail accuracy, and penalizes blurriness and pixelation much more than hallucination. The left plot of Figure 10 shows that this results in the reconstructed images from our direct postprocessing technique having worse LPIPS than the state-of-the-art postprocessing technique. In contrast, the right plot of Figure 10 shows that our results achieve comparable SSIM, a metric that does not penalize blurriness as much as LPIPS.

## 5 Conclusions and Future Work

We have demonstrated that a curious client attacker can successfully reconstruct high-quality images from a small number of clients simply by participating in two consecutive training rounds. This type of attack does not alter the training process or introduce corrupted data, making it difficult to detect by the server or other clients in the system. Our findings indicate that the attack is particularly effective when the number of participating clients is small or the available training examples are limited. This raises a significant concern for cross-silo FL, where participants collaborate specifically to overcome data scarcity (Li et al., ). In such settings, our findings reveal a serious privacy risk, as the reconstruction of sensitive data becomes more feasible. Further research is needed to assess the robustness of more advanced FL frameworks against curious client attacks and develop effective defenses to preserve data privacy in cross-silo FL. Additionally, we assume that the attacker knows the number of participating clients and that each client performs local training on the same number of images. This simplifies the attack framework but does not need to be true. Evaluation with variable client batch sizes and where the attacker does not know the number of participating clients is a promising direction for future research.

Figure: Figure 11: Pixel-level-correct reconstructed images using (row 2) our direct postprocessor and (row 3) ROG GAN (Yue et al., ). Our direct postprocessing method produces reconstructions that are more recognizable and have more accurate image details compared to the ROG GAN. However, our method also results in worse LPIPS and SSIM due to blurring.
Refer to caption: x10.png

Figure: Figure 12: (a) Evaluation on MNIST of the attack with MDE postprocessing compared to (b) the model inversion (MI) and model inversion with Hilbert–Schmidt independence criterion (MI-HSIC) approaches [reproduced from Wu et al. ()]. Only 5 examples were provided for each method in Wu et al. . Our reconstructed images are qualitatively more similar to the ground truth.
Refer to caption: x11.png

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 3
presentation: 3
questions: How does the proposed approach perform with a larger number of clients?
How robust is the proposed approach if any of its underlying assumptions are violated?
soundness: 2
strengths: The strength of this paper lies in its successful implementation of an attack capable of reconstructing images from other participating users. The experiments effectively demonstrate the effectiveness of the proposed method.
summary: This paper proposes an attack approach within the federated learning (FL) framework to reconstruct image data from participating peers in a centralized system. The study demonstrates that consecutive updates in the FL setting can inadvertently reveal information about other clients. Experiments are conducted to validate the effectiveness of this attack method.
weaknesses: I have several concerns regarding the experimental setup and the novelty of this paper, which I outline below:

The method relies on several assumptions, including that each client has ample computational resources, employs a consistent learning rate, and trains with an equal number of images locally. Additionally, the approach presumes that the attacker is either aware of or can accurately estimate the number of clients participating in each training round. Further assumptions, such as the use of full-batch gradient descent for local training and other idealized conditions, may not be realistic for federated learning (FL) environments.

These restrictive assumptions may limit the method's practical applicability in typical FL settings. Federated learning is generally designed to support users with limited resources, accommodate non-iid (non-independent and identically distributed) data, and handle asynchronous updates among clients. The paper does not address these critical FL challenges, potentially reducing the relevance of the proposed approach in real-world scenarios.

The optimization framework introduced here does not appear to be novel, and the paper lacks citations to previous work on similar frameworks. There is also no comparison provided to demonstrate why or how the proposed optimization function is more effective or advantageous over existing methods.

In the experiments, the maximum number of clients is set at 8, which limits the insights into how the framework performs at larger scales. Additionally, there are insufficient ablation studies to illustrate the robustness of the proposed framework under varying conditions.

---

confidence: 5
contribution: 2
initial_rating: 5
presentation: 3
questions: 1. Figures 2, 3, 5, and 8 demonstrate the effect of data reconstruction. What hyperparameters such as the training model structure, batch size, and epoch of FedAvg local training are used corresponding to these results?

2. You selected LPIPS as the main evaluation metric. Do the results or trends of MSE, PSNR, SSIM and LPIPS are consistent in these experiments? Because sometimes the LPIPS values ​​of two sets of images may be close, but the visual effects are very different.

3. In the left figure of Figure 6, when there are 8 clients and the batch size is 64, the attacker has to restore a total of (amazing) 512 images. What is the specific visualizatioin results of these images? Does the LPIPS value reflect the actual reconstruction effect?

4. In Equation (3), why do you choose L2 norm instead of cosine similarity? In your method, which one do you think has a greater impact on the final restoration result, raw reconstruction or post-processing?

5. Figure 2 shows the results of different epochs. How do you choose the best epoch? For all the images to be processed, do they use the same optimal number of epochs?

6. After adding two diffusion models (MDT and DDPMs) to optimize the reconstruction results, how much will the efficiency of the attack and the computational cost increase compared to before?
soundness: 3
strengths: 1. **Stealth and Undetectability**: The attack method does not disrupt the training process or introduce corrupted data, making it challenging for detection by servers or other clients, which underscores its potential impact.

2. **Relevance to Cross-Silo FL**: The findings are particularly concerning for cross-silo FL, where data scarcity is addressed through collaboration, emphasizing the need for enhanced privacy measures in such settings.

3. **Extensive Experiments**: The paper conducts thorough experiments to validate the effectiveness of the attack, providing strong empirical evidence of the vulnerability in FL systems.
summary: This paper investigates the privacy issues in federated learning (FL), a framework allowing nodes to train models locally while sharing updates. Despite its privacy goals, FL is vulnerable to data reconstruction attacks. The paper reveals that not only central servers but also semi-honest clients can reconstruct peers' image data, posing significant risks. Using advanced diffusion models, the authors show how a single client can enhance image quality, underscoring the need for stronger privacy measures to prevent client-side attacks in FL.
weaknesses: 1. This paper attacks from the perspective of any node/client and reconstruct all training data of all other participants. However, this is no different from a conventional inversion attack launched from the server.
When the secure aggregation protocol is applied, the server can obtain the model parameters at time $t$ and the corresponding aggregated gradients; while any client can receive the model parameters at time $t$ and time $t+1$. Obviously, the information obtained in these two cases is exactly the same, and the updated gradient is the difference between these two rounds.
It is good that the authors start from the node/client perspective, but the current analysis is the same as the typical gradient inversion attacks, and there is no special or new inspiration.

2. The core contribution of the paper is to propose a post-processing method for reconstructing images (based on the diffusion models). However, this is based on a premise that the original restored image already contains enough information.
If the results after the attack are like the results of Figure 5(b) and Figure 15 of ROG or the last three rows of Figure 4 of GradInversion (See through Gradients, Yin et al.), the reconstructed images are similar to noise, then your proposed method obviously does not work. How do you solve this situation? This is not mentioned in the paper.

---

confidence: 5
contribution: 1
initial_rating: 3
presentation: 2
questions: My concerns are detailed in the WEAKNESS part, specifically in relation to these limitations. If the authors provide convincing responses to these limitations, I would be happy to raise my score.
soundness: 2
strengths: This paper integrates gradient inversion attacks with generative models to achieve higher quality privacy attacks. Additionally, it takes into account attacks from peer nodes, making the scenario more versatile compared to traditional ones.
summary: This paper proposes a high-quality privacy data reconstruction method in the federated learning scenario, which achieves excellent results especially when the number of participating training nodes and the amount of data are limited. Unlike traditional methods, this paper considers attacks from peer nodes, making the scenario more versatile.
weaknesses: The authors claim that the advantage of this paper lies in the achievement of node-level privacy attacks in the federated learning scenario. However, there are several significant limitations:
1. Unreasonable assumptions. In the aggregation of global gradients, updates from different nodes are weighted based on the amount of training data used by each party. However, the authors simplistically assume that the parties aggregate with equal weights. Additionally, the authors mention that an attacker can directly initialize N (line 161), but we do not think that a peer node can have this information. In summary, these assumptions make the paper fundamentally similar to traditional privacy attacks conducted by a central server, with only an additional simple subtraction operation. This also makes the paper’s main contribution less solid.

2. Lack of novelty and originality. The work presented in this paper is a simple combination of gradient inversion attacks with super-resolution and denoising techniques, without proposing a new method or solving an unsolved problem (although the authors claim to have achieved peer node attacks, we have already shown in point 1 that this assumption is not fundamentally different from existing center-based studies). Therefore, the academic value of this paper is not sufficient for publication in a top-tier conference like ICLR.

3. The introduction of the experimental setup is very rough. In the optimization objective Eq. (3) of this paper, there is no variable related to labels, and the setup does not explain how the attacker obtains the labels. Although the paper mentions the method of Yin et al., their method cannot handle scenarios with duplicate labels. Moreover, I do not think that the paper can be replicated based on the setup provided, as almost all configurations related to optimization are missing.

Meta-review: The paper proposes and demonstrates a gradient inversion attack in federated learning by a client node against other client nodes.
Demonstrating an attack in a novel setting is potentially very interesting.
The reviewers criticise the paper for unrealistic assumptions and limited methodological novelty.
The authors' response seeks to address the reviewer concerns, but mostly fails to make substantial changes.
Unlike many of the reviewers, I agree with the authors' view that demonstrating an attack in the client-vs-client scenario is potentially interesting. However, I feel that the current manuscript provides a very incomplete view of the risk, as the attack requires highly unrealistic assumptions of a client being able to guess certain parameters of the algorithm. In order to demonstrate the authors' claim, it would be important to conduct additional experiments to evaluate the sensitivity of the method to these assumptions. As the current manuscript fails to provide this insight and mainly demonstrates the feasibility of the attack in an ideal scenario, I feel it is not ready for publication at ICLR.

Decision: Reject