Poisoning with A Pill: Circumventing Detection in Federated Learning

## Abstract

Abstract Without direct access to the client’s data, federated learning (FL) is well-known for its unique strength in data privacy protection among existing distributed machine learning techniques.
However, its distributive and iterative nature makes FL inherently vulnerable to various poisoning attacks, including model poisoning attacks and data poisoning attacks . To counteract these threats, extensive defenses have been proposed to filter out malicious clients, using various detection metrics.
Based on our analysis of existing attacks and defenses, we find that there is a lack of attention to model redundancy . In neural networks, various model parameters contribute differently to the model’s performance. However, existing attacks in FL manipulate all the model update parameters with the same strategy, making them easily detectable by common defenses. Meanwhile, the defenses also tend to analyze the overall statistical features of the entire model updates, leaving room for sophisticated attacks.
Based on these observations, this paper proposes a generic and attack-agnostic augmentation approach designed to enhance the effectiveness and stealthiness of existing FL poisoning attacks against detection in FL, pointing out the inherent flaws of existing defenses and exposing the necessity of fine-grained FL security.
Specifically, we employ a three-stage methodology that strategically constructs, generates, and injects poison (generated by existing attacks) into a pill (a tiny subnet with a novel structure) during the FL training, named as pill construction , pill poisoning , and pill injection accordingly.
Extensive experimental results show that FL poisoning attacks enhanced by our method can bypass all the popular defenses, and can gain an up to 7x error rate increase, as well as on average a more than 2x error rate increase on both IID and non-IID data, in both cross-silo and cross-device FL systems.

## 1 Introduction

With the prosperity of machine learning and cloud computing, FL [, ] has been widely recognized as an effective approach to training machine learning models via distributed data on a large number of scattered clients.
Compared with traditional centralized machine learning that collects data to a central cluster, FL does not require direct access to clients’ data, saving the communication overhead while protecting data privacy.
However, due to its distributed architecture, FL is inherently vulnerable when clients are compromised by attackers. Numerous studies [, , , , , ] have demonstrated techniques that manipulate the behavior of trained global model via malicious clients, commonly referred to as poisoning attacks.
These attacks can be further divided into the following two types: 1) Model poisoning attacks involve direct modifications of local model updates by the attacker [, ]. The corrupted updates, when aggregated, significantly skew the global model’s parameters to the opposite direction. 2) Data poisoning attacks inject malicious samples into the local training datasets [, , , , , , , ]. The compromised data samples lead to corrupted local model updates that, once merged, secretly deteriorate the global model’s integrity.
Poisoning attacks expose severe security risks to FL [, , , ], violating its integrity and reliability.

To alleviate and further eliminate the impacts caused by poisoning attacks, researchers have proposed various defense methods. They can be divided into four main categories, including adaptive client filtering, statistical parameter aggregation, client-dominant detection, and other advanced metrics and pipelines. Adaptive client filtering [, , , , , ] utilizes various metrics or scores to evaluate the risk of clients, and discards updates from those
with a high risk level. Statistical parameter aggregation [, , , ] modifies the standard aggregation process of FL, granting different weights to different clients based on their model update’s statistical features. Client-dominant detection [, , , , ] as a rising category leverages clients to maintain the integrity of FL training. While other advanced metrics and pipelines [, , , , ] use other advanced features or pipelines to perform the detection.
The effectiveness of existing defense methods hinges on their ability to detect abnormal parameter updates, which are obvious when using most existing attacks.
In particular, these attacks treat all neural network parameters equally and manipulate the entire model update using the same mutation strategy.

We argue that it is inefficient and ineffective for attackers to modify all the parameters simultaneously. Researches on model pruning [, , , , ] show that model parameters do not contribute equally to a model’s performance. Parameters that have little impact on a model’s performance are considered redundant. Manipulating these redundant parameters not only wastes the attacker’s budget but also decreases the stealthiness of the attack, especially when the FL server analyzes the overall statistics of model updates for anomaly detection. Thus, a more effective strategy is to manipulate
critical parameters [], which are crucial for the model’s performance. This approach ensures that the changes have a significant impact, enhancing the attack’s effectiveness while maintaining stealthiness.

We propose a novel attack-agnostic augmentation method in this paper, enhancing existing model poisoning attacks via a three-stage pipeline, including pill construction, pill poisoning, and pill injection.
In the first stage – pill construction, we carefully design an abstract architecture of pill (a tiny subnet with a unique structure), named the pill blueprint, and identify the corresponding pill subnet instance in the target model via a dynamic search algorithm.
Then in the second stage – pill poisoning, we
reuse existing FL poisoning attacks in an attack-agnostic fashion and concentrate the malicious model updates to the selected pill subnet.
Finally, in the pill injection stage, we insert the poisoned pill into a
benign model update and use a two-step adjustment to further reduce the difference between the poisoned model update and the original benign update.
To this end, we augment existing FL poisoning attacks by dynamically generating a pill, poisoning it, and gradually inserting it into the global model.

We conduct extensive experiments to evaluate the effectiveness of our augmentation method. We use our method to enhance four baseline poisoning attacks,
namely, sign-flip attack, trim attack [], krum attack [], and min-max attack []. Using both the original and augmented attacks, we test the error rates (i.e., the proportion of incorrect predictions) of the global model trained by nine aggregation rules, including FedAvg [], FLTrust [], Multi-Krum [], Median [], Trim [], Bulyan [], FLDetector [], DnC [], and Flame []. The set of aggregation rules covers most existing defense metrics, to the best of our knowledge. We also design a corresponding adaptive defense to further test the robustness of our method when the defender has the full knowledge of our pipeline and detailed implementation.
The experimental results illustrate that our method can substantially improve existing FL poisoning attacks by inducing a more than 2x increase on average in the model prediction error rate under existing defenses, and at most, a more than 7x error rate increase.

Our contributions can be summarized as follows:

- •
We propose a generic and attack-agnostic augmentation approach designed to enhance poisoning attacks against robust FL. We are the first to encapsulate model poisoning attacks to well-defined subnets (i.e., pills) combined with comprehensive metric-based performance-boosting adjustment.
- •
Extensive results against nine aggregation rules on three common datasets show the attack enhancement capability of our method. In particular, it helps four baseline model poisoning attacks bypass almost all the prevailing defense methods.
- •
We observe and point out the inherent limitations of existing model poisoning attacks and defenses, exposing the necessity and potential of fine-grained FL security.

## 2 Background and Related Work

### 2.1 Federated Learning

Federated Learning (FL) [, ] trains a global model using the information from a swarm of clients without the direct access to each client’s data.
In a standard FL training process, within an arbitrary communication round $t$, the FL server first distributes its global model $\boldsymbol{g}_{t}$ to all the clients $K$.
After receiving this global model, each client $i$ trains a local model $\boldsymbol{g}_{t}^{(i)}$ with its local data $D^{(i)}$, and uploads the model update $\Delta\boldsymbol{g}_{t}^{(i)}$ to the FL server.
After receiving the model updates from the clients, the FL server uses aggregation rules to calculate the global model $\boldsymbol{g}_{t+1}$ for the next round.
The objective of FL can be formulated as:

$$ $\mathop{min}_{\boldsymbol{g}}\sum_{i=0}^{K}\frac{|D^{(i)}|}{|D|}\cdot f(D^{(i) },\boldsymbol{g}).$ (1) $$

### 2.2 Poisoning Attacks in FL

Based on prior investigations [, , ], existing poisoning attacks in Federated Learning (FL) can be classified according to the techniques employed by attackers. Attackers may directly compromise the global model by manipulating the updates from local models [, , , , ] by compromised clients. Alternatively, they may poison their local datasets to indirectly influence the global model [, , , , , ]. The former technique is referred to as model poisoning attacks in FL, while the latter data poisoning attacks.
Although model poisoning attacks are effective, existing attacks have limited stealthiness and can be detected by many existing defenses. Our goal is hence to demonstrate that such attacks can be augmented in a uniform way.
Model poisoning attacks directly manipulate the parameters uploaded by clients, with a minimal interference to the local training process. Among these attacks, the simplest form is the sign-flipping attack, which directly flips the model update and scales it with a constant factor.
A-Little-is-Enough [] generates malicious updates within a calculated perturbation range to deceive the global model.
Adaptive attacks [], such as the Trim attack and the Krum attack, dynamically scale malicious updates based on parameter values and distances. The Min-Max and Min-Sum Attacks [] provide a dynamic scaling for malicious updates based on different distance-based criteria. MPAF [] aims to drive the global model towards a predefined target model with poor performance on given FL tasks. For the sake of generality, we employ the sign-flipping attack, two types of adaptive attacks [] (the Trim attack and the Krum attack), and the Min-Max attack [] as the baseline attacks in this paper.

Additionally, our method’s pill design is inspired by a specialized data poisoning attack known as the subnet replacement attack (SRA) []. This approach concentrates backdoor attacks within an arrow-width subnetwork of the original model. It trains this selected subnet using poisoned data and replaces the corresponding parameters of the target model with those from the trained subnetwork. Once the replacement is complete, SRA severs the connections between the poisoned subnetwork and the original model to preserve the efficacy of the attack. The stealthy yet effective design of SRA inspire our method.
In particular, we devise a new subnet structure, referred to as the pill blueprint, which features heterogeneous widths to better accommodate a variety of existing FL poisoning attacks. Besides, unlike SRA’s one-time injection, our method gradually poisons the global model throughout the entire FL training, achieving better effectiveness against a wide range of defenses in the FL setting.

Figure: Figure 1: Overview of our augmentation method. The red parts indicate our augmentation method’s contribution, and the cyan parts represent the standard federated learning architecture.
Refer to caption: x1.png

### 2.3 Defenses against Poisoning Attacks in FL

Existing defenses can be categorized based on the mitigation strategies that they utilize.

Adaptive Client Filtering.
These techniques such as Krum and Multi-Krum [] filter out malicious clients through single or multiple rounds of client selection based on distance scores. FLTrust [] computes trust scores using the cosine similarity between each client update and the server model’s update for weighted averaging. SignGuard [] employs sign-based clustering combined with norm-based thresholding to identify and filter malicious clients. Flame [] and Deepsight [] propose adaptive clustering and clipping to safeguard against backdoor attacks. SkyMask [] clusters trainable feature masks of clients to assess each client’s risk level.

Statistical Parameter Aggregation.
Approaches like Median and Trim [] use coordinate-wise median or trimmed mean values to aggregate model updates. Bulyan [] enhances robustness by integrating Krum with Trim techniques. Fool’s Gold [] applies an adaptive learning rate based on inter-client contribution similarity to mitigate the effects of malicious updates. SparseFed [] aggregates sparsified updates, reducing the risk of model poisoning attacks.

Client-dominant Detection.
Siren [] and Siren^+ [] set proactive accuracy-based alarms at the client level with the corresponding server-side decisions to counter various model poisoning attacks. FL-WBC [] introduces client-side noise to diminish the efficacy of attacks and shorten their duration. FLIP [] achieves higher robustness through client-side reverse-engineering defenses against extensive poisoning strategies. LeadFL [] uses a client-side Hessian matrix optimization to reduce the impact of adversarial patterns on backdoor and targeted attacks.

Other Advanced Metrics and Pipelines.
Various studies employ other sophisticated metric pipelines designed for detection to ensure robust defense against poisoning attacks. These include techniques proposed in studies such as Zeno [], CRFL [], FedRecover [], FLCert [], FLDetector [], and MESAS [].

To comprhensively evaluate our method, we use Multi-Krum (MKrum) [], Trimmed Mean (Trim) [], Coordinate-wise Median (Median) [], Bulyan [], FLTrust [], FLDetector (FLD) [], DnC [], and Flame [], a set of representative methods, as our baselines. More details are shown in Appendix A.

## 3 Threat Model

Attacker’s Goal and Capabilities.
This paper focuses on improving the effectiveness of existing poisoning attacks in FL. Similar to previous work [, ], an attacker aims to raise the error rates of the global model on a specific class or multiple classes by sending poisoned model updates via compromised clients during the iterative aggregation.
Our method does not require any additional knowledge compared with existing FL poisoning attack. Hence we reuse the typical threat model in existing studies [, ].
The attacker has a complete control of the compromised clients, including their local data, local training, and uploading process. With the aggregated resources on the compromised clients, the attacker may aggregate the local data from the compromised clients to do extra training or aggregate their local updates to do model estimation.
The attacker may or may not know the updates of other benign clients, depending on the confidentiality of the communication channels between the server and clients.
Besides, the attacker cannot access the server’s information, including the aggregation rules or the selected clients in each round.

Defense Settings.
Most of the defenses in FL are deployed and executed on the server. We adopt a similar defense setting as existing studies [, , , ].
The server cannot directly analyze the local data or the local training of clients. It can only detect malicious clients through model updates from different clients. The server can collect and possess a root test dataset to provide more accurate and robust detection, while the data of such a root test dataset cannot be derived from clients. The data distribution of this root test dataset may or may not be the same as the data distribution across the clients.

## 4 Design Objectives and Challenges

After analyzing the drawbacks and various implementations of existing FL poisoning attacks, we define three main objectives for our attack augmentation method:
1) For stealthiness, the augmentation method should stay stealthy
while achieving
comparable performance with original attacks.
2) For compatibility, the augmentation should be compatible with most of the existing FL poisoning attacks with few modifications on their implementations.
3) For generality, the attack augmentation should be able to bypass general detection methods with different detection metrics.

Corresponding to each objective, three challenges need to be addressed:

- •
It presents a significant challenge that the
attack augmentation method must use significantly fewer parameters while still achieving similar results as the original attacks.
- •
It is
challenging to develop a uniform augmentation method for
various FL poisoning attacks since they require different information and are implemented in different training stages.
- •
It is difficult
to devise a general strategy that bypasses all common
detection approaches, while guaranteeing the attack effectiveness.

## 5 Design

**TABLE I: Main notations. Symbols in the gray part are used for attacks.**
| Symbol | Meaning |
| --- | --- |
| $T$ | Total FL communication round |
| $t$ | FL communication round index |
| $K$ | Total client number |
| $\boldsymbol{g}$ | Global model of the FL training |
| $\boldsymbol{g}_{t}$ | Global model in round $t$ |
| $lr$ | Learning rate |
| $f()$ | Loss function used in the FL training |
| $i$ | Client index |
| $E$ | Local training epoch number |
| $D^{(i)}$ | Local training data on client $i$ |
| $\Delta\boldsymbol{g}_{t}^{(i)}$ | Local model update of client $i$ in round $t$ |
| $m$ | Total amount of malicious clients |
| $D^{m}$ | Aggregated data from compromised clients |
| $\Delta\hat{\boldsymbol{g}}_{t}^{m}$ | Update of extra-trained model in round $t$ |
| $\Delta\boldsymbol{\widetilde{g}}_{t}$ | Estimated global model update in round $t$ |
| $\Delta\boldsymbol{g}_{t}^{zero}$ | Disconnection update in round $t$ |
| $M$ | Selected malicious subnetwork |
| $M_{disc}$ | Disconnection mask corresponding to $M$ |
| $C_{iter}$ | Max malicious update adjustment iteration |
| $C_{\uparrow}$ | Up-scaling factor |
| $C_{\downarrow}$ | Down-scaling factor |

Figure: Algorithm 1 Our augmentation method’s workflow. Without losing generality, we assume the first $m$ clients as malicious clients.

### 5.1 Overview of Our Method

Fig. 1 presents the three key stages. Table I presents notations of main symbols in this paper.

Stage \small1⃝: Pill Construction. It leverages a dynamic subnetwork search algorithm to achieve stealthiness by selecting the poison pill from the global model $\boldsymbol{g}_{t}$, considering the importance of model’s parameters. Since
the global model continuously changes across rounds, it is hard to have a fixed pill pattern.

Stage \small2⃝: Pill Poisoning. In this state, we reapply existing FL poisoning attacks to the selected poison pill, using an extra trained model $\boldsymbol{\hat{g}^{m}_{t+1}}$ (trained on data from the compromised clients) as the attacker’s base model. For compatibility, we only modify the input of the existing FL poisoning attacks and utilize their outputs, without any interference to their internal implementations. This black-box utilization lets our method be attack-agnostic and compatible with most of the existing FL poisoning attacks.

Stage \small3⃝: Poison Pill Injection. It contains poison pill insertion & disconnection, and poison pill adjustment. In this stage, our augmentation method injects the poison pill into the estimated benign update $\Delta\boldsymbol{\widetilde{g}}_{t+1}$, and further adjusts the boosting magnitude of both the poison pill parameters and the remaining parameters. We propose a two-step dynamic adjustment to enhance the generality of our method against most defenses.

We are the first that propose a universal attack augmentation pipeline for most FL poisoning attacks, considering stealthiness, compatibility, and generality. The detailed workflow of our method is shown in Algorithm 1.

### 5.2 Pill Construction

Figure: Figure 2: An example of the “approximate max pill search” algorithm in our augmentation method.
Refer to caption: x2.png

This stage aims to construct a pill structure for augmenting the stealthiness while retaining the attack effectiveness before being augmented. The pill is carefully crafted to involve a minimal subset of parameters from specific positions of the target model.
We first define a pill’s blueprint as the pill’s graphic structure, independent of target model parameters.
Then, we propose a dynamic pill search algorithm to identify and map concrete parameters from the target model to the blueprint.

Designing Pill Blueprint.
The blueprint design is inspired by SRA [], which shows that poisoning a narrow subnetwork (one neuron/channel in each layer) is adequate to effectively inject backdoors into machine learning models (not in the FL setting).
However, their technique cannot be
used for our purposes as their subnet architecture is very specific. It does not support attacking various targets; it is a fixed and pre-selected subnet without considering the dynamics of model training in FL; and its poisoned subnet is not stealthy, having substantially larger weight values compared to others due to the need to disseminate the poison effect through such a small pre-selected network.
Therefore, we propose a novel blueprint method, in which the subnet structure is general, and its instantiations (i.e., the concrete subnets) vary across steps in the FL training procedure, including important neurons by a dynamic search algorithm. This allows small weight changes because poisoning important neurons enables easy dissemination, maximizing
attack
stealthiness.
In particular, the pill blueprint is designed to accommodate various target classes of different FL poisoning attacks. It achieves this by manipulating the outputs relevant to multiple classes simultaneously, via disrupting all the output neurons together.
Hence, our pill blueprint design follows the rules below:

- 1.
The pill blueprint only contains one neuron in each linear layer or one channel in each convolutional layer, except for the last two layers. Suppose $\mathcal{N}_{i}^{p}$ represents the neuron/channel number in Layer $i$ in our pill blueprint, then $\mathcal{N}_{i}^{p}=1$ when $i<L-1$, where $L$ is the total layer counts in our pill blueprint.
- 2.
In the last two layers of our pill blueprint, $\mathcal{N}^{p}_{L-1}=\mathcal{N}^{p}_{L}=$ number of classes.

Dynamic Pill Search.
According to existing studies on neural network pruning [, , , , ], parameters with a larger magnitude typically dominate the model’s performance.
The optimal solution is hence to examine the model parameters to search for a globally optimal pill that encompasses the most important parameters.

However, such a globally optimal pill could be identified via a pruning-based method [, ], and hence our attack could be easily detected. Besides, searching for a globally optimal pill is inefficient when the model has a large number of parameters. Thus, we search for an approximate
pill instead, with an attacker-defined start point, and only evaluate a small subset of the entire model’s parameters. We name the search algorithm as “approximate max pill search”.
A complete procedure of this search algorithm consists of the following four steps. To improve readability, we use “neuron” to represent both neurons in fully connected layers and channels in convolutional layers, and we use the classification task as an example:

At the beginning of the search,
we randomly choose a subset of neurons from the first layer $l_{1}$ of the target model, based on the structure of the first layer $l^{p}_{1}$ in the pill’s blueprint and its neuron number $\mathcal{N}^{p}_{1}$. The selected neurons are termed as $\mathcal{V}_{1}$, and defined as start points, which are then fixed across the entire FL training.

For each subsequent layer $l_{i}$ in the target model, we first calculate the sum of the weights from the selected neurons $\mathcal{V}_{i-1}$ in layer $l_{i-1}$ to each neuron in $l_{i}$. Then, we rank all the neurons in $l_{i}$ based on the parameter value sums and choose top $\mathcal{N}^{p}_{i}$ neurons in $l_{i}$ as the new $\mathcal{V}_{i}$, where $\mathcal{N}^{p}_{i}$ represents the number of neurons in the $i$th layer of pill’s blueprint. $\mathcal{V}_{i}$ and all the parameters from $\mathcal{V}_{i-1}$ to $\mathcal{V}_{i}$ are recorded.

After visiting $l_{L-1}$ layers, where $L$ is the total number of layers in the target model, $||\mathcal{V}_{L-1}||$ should equal to the neuron number in $l_{L}$ of the target model, which also equals to the number of classes. We select all the neurons in the target model’s $l_{L}$ layer into our pill to construct $\mathcal{V}_{L}$. Then, we only record the parameters from one neuron in $\mathcal{V}_{L-1}$ to only one neuron in $\mathcal{V}_{L}$ based on the index order (i.e., the first neuron in $\mathcal{V}_{L-1}$ is paired with the first neuron in $\mathcal{V}_{L}$).
Since $||\mathcal{V}_{L-1}||$ equals to $||\mathcal{V}_{L}||$, the number of recorded parameters equals to the number of classes,
avoiding poisoning too many parameters in a single layer.

With the recorded $\mathcal{V}_{i}$ and the corresponding parameters, we construct
two masks $\boldsymbol{M}$ and $\boldsymbol{M}_{disc}$. The mask $\boldsymbol{M}$ records the pill’s parameters in the target model, and the disconnection mask $\boldsymbol{M}_{disc}$ records the parameters of the connections between the pill and the rest of the target model. $\boldsymbol{M}$ is used for poisoning,
while $\boldsymbol{M}_{disc}$ is used to disconnect the poison pill from the target model, maintaining the integrity and performance of the pill during poisoning.
The two masks have the same shape as the target model’s parameters.
To construct $\boldsymbol{M}$, we set the locations corresponding to the pill parameters to $1$, and the others to 0 0.
Based on $\boldsymbol{M}$, we can similarly obtain the corresponding disconnected mask $\boldsymbol{M}_{disc}$, which sets the locations corresponding to the parameters from neurons other than $\mathcal{V}_{i-1}$ to $\mathcal{V}_{i}$ in each model layer $l_{i}$ (except for the $L$th layer since we choose all the neurons from it), and also those corresponding to parameters from $\mathcal{V}_{i-1}$ to other pill irrelevant neurons in each model layer $l_{i}$
to $1$.
The two masks are used in the Pill Injection Stage.

Example.
Fig. 2 presents a concrete example of the search algorithm in a 4-layer linear model. Since the start point is randomly
selected by the attacker, defense methods can hardly guess it without any prior knowledge.
In the example, suppose the model is a 4-layer linear models for a binary classification task. Then the pill blue print contains one neuron in the first two layers, and contains two neurons in the last two layers. Initially, we randomly select a start neuron, specifically the second neuron in the first layer in the example. Then, we conduct layer-wise searching when visiting the second and third layer, selecting the parameters with the highest magnitudes. At the forth layer (output layer), we pair the two output neurons with the two selected neurons in the third layer based on the index order. And we finally construct two pill-related masks accordingly.

With the search algorithm,
we also reduce the complexity of the pill search from $\mathcal{O}(\prod_{1}^{L}\mathcal{N}_{i})$ to $\mathcal{O}(\sum_{1}^{L}\mathcal{N}_{i}\cdot\mathcal{N}^{p}_{i})$, where $\mathcal{N}_{i}$ represents the neuron number of the target model in layer $i$, and $\mathcal{N}^{p}_{i}\ll\mathcal{N}_{i}$ for all the hidden layers. The computational complexity of our pill search is hence much smaller than the computational complexity of one round local training.

**TABLE II: Different dynamic patterns in our augmentation method, utilizing along with the max subnetwork searching.**
| Notation | Description |
| --- | --- |
| $\textsc{Pattern}_{1}$ | All layers use adaptive searching strategy |
| $\textsc{Pattern}_{2}$ | All layers use one-time searching strategy |
| $\textsc{Pattern}_{3}$ | \Centerstack[l]FE uses with adaptive searching strategy |
| CLS uses repeated searching strategy |  |
| $\textsc{Pattern}_{4}$ | \Centerstack[l]FE uses repeated searching strategy |
| CLS uses with adaptive searching strategy |  |
| $\textsc{Pattern}_{5}$ | \Centerstack[l]FE uses with adaptive searching strategy |
| CLS uses with one-time searching strategy |  |
| $\textsc{Pattern}_{6}$ | \Centerstack[l]FE uses with one-time searching strategy |
| CLS uses with adaptive searching strategy |  |

To make the pill search process dynamic, we also design several patterns to adaptively determine whether to change the pill in the training period, shown in Table II (FE represents the convolutional layers, CLS represents the linear layers). For more details about each specific dynamic pattern, please refer to Appendix C. The combination of the “approximate max pill search” with different dynamic patterns construct the complete dynamic pill search, considering both the stealthiness and the efficiency.

Figure: Figure 3: Cosine similarities between FLTrust server’s model update and malicious model update when malicious clients use different extra training rounds.
Refer to caption: x3.png

### 5.3 Pill Poisoning

In the pill poisoning stage, we aim to condense the poison into the pill using existing attacks.
To achieve compatibility, our method simply reuses existing FL poisoning attacks, without any intrusive modification to their original implementations. We only modify the input of existing FL poisoning attacks by replacing the base model update with the update from a model that has undergone extra training rounds, denoted as $\Delta\boldsymbol{\hat{g}^{m}_{t+1}}$. Additionally, we restrict changes to parameters within the pill.
The output is a poisoned pill
that will be used in the next pill injection stage.

The motivation to use an extra-trained model update as the reference model update is shown in Fig. 3. As shown in the figure, with the increasing number of extra training rounds on the malicious clients, the generated malicious model update becomes less opposite to the FLTrust []
server’s model update.
Thus, we adopt the extra training in our method and limit the extra training epoch number $E_{extra}$ to less than the number of malicious clients $m$ times the benign local training epoch number $E$, denoted as $E_{extra}\leq m\cdot E$. With this extra training epoch number limit, we do not violate the threat model since the attacker can utilize the data and computational resources of all the compromised clients.

### 5.4 Pill Injection

In the pill injection stage, we aim to inject the pill into the
model
and use a two-step adjustment method to further camouflage the pill. Thus, the entire injection stage could be divided into two parts - pill injection
and camouflaging.
After this stage, the poison pill is
seamlessly integrated
with
the benign model update and uploaded to the FL server.

Pill Insertion & Disconnection.
In this part, our goal is to insert the pill
into the model, and minimize the impact of the benign model updates on our pill. We use an estimated global model update as the benign model update, which is estimated as the coordinate-wise mean values of all the normal model updates from the compromised clients. The estimation process in the Estimation() in Algorithm 1 Line 11 is hence presented as Equations (2),

$$ $\displaystyle\Delta\boldsymbol{\widetilde{g}}_{t+1}$ $\displaystyle\leftarrow\mathop{mean}\{\Delta\boldsymbol{g^{\prime}}^{(1), \cdots,(m)}_{t+1}\},$ (2) $$

where $\Delta\boldsymbol{g^{\prime}}^{(i)}_{t+1}$ is the normal updates from the compromised clients. By aggregating information from multiple malicious clients, the estimated benign global model update is more similar to the genuine one, providing more budget for our poison pill.

After obtaining the estimated global model update $\boldsymbol{\Delta\widetilde{g}}_{t+1}$, we directly replace the
parameters corresponding to the pill parameters (which have been poisoned in the previous stage)
via the pill’s mask $\boldsymbol{M}$. Then, we replace the parameters that connect the pill and the other estimated global model updates with the disconnection update $\Delta\boldsymbol{g}_{t+1}^{zero}$, using the disconnection mask $\boldsymbol{M}_{disc}$. The disconnection update $\Delta\boldsymbol{g}_{t+1}^{zero}$ is calculated as $0-\boldsymbol{g}_{t}$, and is bounded by the maximum and minimum values of the reference model update $\Delta\boldsymbol{\hat{g}^{m}_{t+1}}$. The disconnection update can gradually change the parameters of the connections between the pill and rest of the model to 0 0, and finally isolates the poison pill from the global model, guaranteeing the attacking effects of the poison pill.

Figure: Algorithm 2 Similarity-based and distance-based adjustment functions in the Poison Pill Injection stage.

Figure: Figure 4: Intuition behind distance-based adjustment in our augmentation method.
Refer to caption: x4.png

Pill Adjustment.
After the injection,
we use a two-step adjustment to further adjust the pill, improving the generality against multiple detection metrics simultaneously.
In this stage,
we consider two prevailing detection metrics – distance and cosine similarity. To increase the cosine similarity between the poisoned model update and the benign model update in our method, we balance the magnitudes of both the poison pill’s parameters and the other benign parameters.
Similarly, to minimize the distance discrepancy between the poisoned and benign model updates, we adjust the magnitude of the entire poisoned model update. Thus, we first use the similarity-based adjustment, then use the distance-based adjustment to balance the effectiveness and stealthiness poisoned model update. This two-step adjustment is particularly effective when combined with our method, which selectively poisons only a tiny subset of the model’s parameters. By altering just a few parameters, our method preserves a substantial number of benign parameters, which are crucial for making effective adjustments. As a result, the poisoned model update can bypass a wide range of defenses since they are typically designed based on the combination or variants of distance and cosine similarity metrics, and they usually do not anticipate such a focused and minimal interference in the model parameters. The details of the two seamless adjustments are as follows:

As shown in Line 1--12 of Algorithm 2, we first compute the maximum cosine similarity $S_{max}$ between the normal model updates from the compromised clients and the estimated global model update in the current round. Then, we iteratively and alternately reduce the magnitude of the poison pill’s parameters with the down-scaling factor $C_{\downarrow}$, and increase the magnitude of the rest estimated global model update’s parameters with the up-scaling factor $C_{\uparrow}$, until the cosine similarity between the entire poisoned model update and the estimated global model update is greater than $S_{max}$ or the adjustment total iteration is greater than the threshold $C_{iter}$.

In the Distance-based Adjustment (Line 13--24 of Algorithm 2), we reuse the up-scaling factor $C_{\uparrow}$ and the down-scaling factor $C_{\downarrow}$ to adjust the magnitude of the entire poisoned model update. The intuition behind the Distance-based Adjustment is shown in Fig. 4. We first calculate the maximum distance between the normal model updates from the compromised clients and the estimated global model update in the current round. We use this maximum distance $Dist_{max}$ as the threshold in the distance-based adjustment. Then, we further determine the scaling factor that should be used by applying the two scaling factors $C_{\uparrow}$ and $C_{\downarrow}$ separately to the poisoned model update $\Delta\boldsymbol{g}_{t+1}^{(i)}$. The scaling factor that reduces the distance between $\Delta\boldsymbol{g}_{t+1}^{(i)}$ and $\boldsymbol{\Delta\widetilde{g}}_{t+1}$ is chosen as the initial scaling factor in the subsequent iterative scaling. We stop the scaling until the distance between the $\Delta\boldsymbol{g}_{t+1}^{(i)}$ and $\boldsymbol{\Delta\widetilde{g}}_{t+1}$ is smaller than $Dist_{max}$, or the scaling factor begins to increase such distance (reach the limit of the scaling).

## 6 Evaluation

This section assesses how our method enhances the effectiveness of existing FL poisoning attacks from multiple perspectives. We begin by evaluating the Augmentation Effectiveness of our method against four FL poisoning attacks, using nine prevailing defenses across three datasets, detailed in Section 6.2. Subsequently, we visualize the Stealthiness of our method under two prevailing detection metrics, as discussed in Section 6.3. Lastly, an Generality Analysis of our method is presented, which includes tests on various proportions of malicious clients, tests on both cross-silo and cross-device settings, and evaluates the impact of different pill search rules, outlined in Section 6.4. Our method significantly enhances the capabilities of existing FL poisoning attacks, successfully bypassing all $9$ baseline defenses in over 90% of cases, and increasing the error rates by up to seven times compared to the original attacks’ error rates. Moreover, it demonstrates robustness across varying data distributions, model architectures, proportions of malicious clients, and pill search rules.

### 6.1 Evaluation Settings

Attack, Defense, and Framework settings.
In our experiments, we typically set the malicious proportion to $20\%$. We implement $9$ baseline aggregation rules, including FedAvg, FLTrust, Multi-Krum, Bulyan, Median, Trim, FLDetector, DnC, and Flame. We use our method to augment $4$ existing model poisoning attacks, including sign-flipping attack, Trim attack, Krum attack, and Min-Max attack. These attacks are chosen for their representativeness in illustrating the effectiveness of our method. We configure a 50-client FL system for both the MNIST and Fashion-MNIST datasets. For the CIFAR-10 dataset, a 30-client FL system is used. Our framework accommodates both cross-silo and cross-device settings. The entire framework is based on PyTorch [].

Model, Dataset, and Hyper-Parameters.
In our experiments, we employ a four-layer Convolutional Neural Network (CNN) and a simplified version of AlexNet []. The structures of the models and their corresponding pill blueprints are detailed in Appendix B. We evaluate our method on three widely-used datasets: MNIST [], Fashion-MNIST [], and CIFAR-10 []. We use the CNN model on MNIST and Fashion-MNIST datasets, and the AlexNet [] on CIFAR-10 dataset. Each experiment is repeated five times to ensure reliability, with the mean and standard deviation (std) of the results reported.

**TABLE III: Error rates under cross-silo setting using “approximate max pill search” (20% malicious clients) on Fashion-MNIST dataset.**
| Data Distribution | IID | Non-IID |  |  |  |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Attack | FedAvg | FLTrust | MKrum | Bulyan | Median | Trim | FLD | FedAvg | FLTrust | MKrum | Bulyan | Median | Trim | FLD |
| No Attack | 0.109 | 0.107 | 0.105 | 0.105 | 0.123 | 0.106 | 0.115 | 0.113 | 0.115 | 0.115 | 0.112 | 0.142 | 0.115 | 0.122 |
| $\pm$0.003 | $\pm$0.003 | $\pm$0.002 | $\pm$0.001 | $\pm$0.004 | $\pm$0.002 | $\pm$0.002 | $\pm$0.002 | $\pm$0.003 | $\pm$0.004 | $\pm$0.003 | $\pm$0.003 | $\pm$0.003 | $\pm$0.003 |  |
| Sign-flipping Attack | 0.943 | 0.114 | 0.108 | 0.126 | 0.136 | 0.116 | 0.118 | 0.917 | 0.126 | 0.117 | 0.132 | 0.152 | 0.124 | 0.127 |
| $\pm$0.023 | $\pm$0.003 | $\pm$0.002 | $\pm$0.001 | $\pm$0.002 | $\pm$0.001 | $\pm$0.003 | $\pm$0.020 | $\pm$0.004 | $\pm$0.002 | $\pm$0.003 | $\pm$0.006 | $\pm$0.003 | $\pm$0.003 |  |
|  | 0.667 | 0.115 | 0.764 | 0.379 | 0.523 | 0.314 | 0.646 | 0.543 | 0.122 | 0.754 | 0.430 | 0.522 | 0.311 | 0.688 |
| + Poison Pill | $\pm$0.089 | $\pm$0.004 | $\pm$0.049 | $\pm$0.104 | $\pm$0.091 | $\pm$0.018 | $\pm$0.061 | $\pm$0.150 | $\pm$0.006 | $\pm$0.129 | $\pm$0.057 | $\pm$0.038 | $\pm$0.038 | $\pm$0.067 |
| Trim Attack | 0.243 | 0.109 | 0.139 | 0.146 | 0.174 | 0.179 | 0.116 | 0.332 | 0.120 | 0.201 | 0.163 | 0.231 | 0.238 | 0.124 |
| $\pm$0.010 | $\pm$0.003 | $\pm$0.002 | $\pm$0.006 | $\pm$0.006 | $\pm$0.003 | $\pm$0.001 | $\pm$0.022 | $\pm$0.005 | $\pm$0.018 | $\pm$0.004 | $\pm$0.008 | $\pm$0.009 | $\pm$0.003 |  |
|  | 0.618 | 0.576 | 0.638 | 0.284 | 0.453 | 0.219 | 0.115 | 0.668 | 0.517 | 0.687 | 0.292 | 0.473 | 0.223 | 0.222 |
| + Poison Pill | $\pm$0.071 | $\pm$0.057 | $\pm$0.041 | $\pm$0.040 | $\pm$0.091 | $\pm$0.010 | $\pm$0.003 | $\pm$0.033 | $\pm$0.038 | $\pm$0.036 | $\pm$0.047 | $\pm$0.047 | $\pm$0.016 | $\pm$0.128 |
| Krum Attack | 0.116 | 0.109 | 0.189 | 0.201 | 0.172 | 0.137 | 0.786 | 0.128 | 0.116 | 0.235 | 0.276 | 0.217 | 0.160 | 0.947 |
| $\pm$0.002 | $\pm$0.003 | $\pm$0.022 | $\pm$0.009 | $\pm$0.008 | $\pm$0.003 | $\pm$0.087 | $\pm$0.004 | $\pm$0.003 | $\pm$0.059 | $\pm$0.003 | $\pm$0.005 | $\pm$0.003 | $\pm$0.030 |  |
|  | 0.735 | 0.155 | 0.715 | 0.422 | 0.578 | 0.310 | 0.637 | 0.716 | 0.151 | 0.737 | 0.468 | 0.730 | 0.334 | 0.690 |
| + Poison Pill | $\pm$0.032 | $\pm$0.032 | $\pm$0.132 | $\pm$0.046 | $\pm$0.057 | $\pm$0.009 | $\pm$0.074 | $\pm$0.104 | $\pm$0.004 | $\pm$0.078 | $\pm$0.017 | $\pm$0.168 | $\pm$0.031 | $\pm$0.079 |
| Min-Max Attack | 0.183 | 0.110 | 0.431 | 0.330 | 0.183 | 0.218 | 0.825 | 0.269 | 0.125 | 0.619 | 0.434 | 0.255 | 0.278 | 0.831 |
| $\pm$0.008 | $\pm$0.002 | $\pm$0.029 | $\pm$0.015 | $\pm$0.009 | $\pm$0.009 | $\pm$0.052 | $\pm$0.026 | $\pm$0.015 | $\pm$0.050 | $\pm$0.080 | $\pm$0.012 | $\pm$0.007 | $\pm$0.049 |  |
|  | 0.702 | 0.303 | 0.668 | 0.327 | 0.514 | 0.314 | 0.778 | 0.629 | 0.320 | 0.612 | 0.406 | 0.547 | 0.376 | 0.822 |
| + Poison Pill | $\pm$0.114 | $\pm$0.201 | $\pm$0.116 | $\pm$0.074 | $\pm$0.053 | $\pm$0.047 | $\pm$0.063 | $\pm$0.114 | $\pm$0.115 | $\pm$0.040 | $\pm$0.065 | $\pm$0.072 | $\pm$0.119 | $\pm$0.036 |

IID and Non-IID Data Settings.
Our method was assessed under both IID and non-IID data distributions to understand its performance across data heterogeneity. For IID data setting, we uniformly split all the training data into $K$ shards, and distribute each shard to a random client. For non-IID data setting, we utilize the non-IID degree $p$ as defined in prior studies [, ]. A higher $p$ indicates greater data heterogeneity among the clients. Specifically, when $p=0.1$, the data configuration is essentially IID. We set $p=0.5$ to to intensify the non-IID condition, under which we create and allocate $K$ non-IID data shards to all the clients, simulating a more realistic and challenging FL environment. Given that FLTrust necessitates a root dataset at the server, we select this dataset first from the available training data. Subsequently, we distribute the remaining data among the clients according to the aforementioned IID and non-IID rules. This approach ensures that there is no overlap between server’s data and client’s data.

Configurations of Dynamic Patterns in Our Method.
As outlined in Section 5.2, we design six dynamic patterns for the pill search. We systematically evaluate all six patterns and present the results of the most effective strategy.

Evaluation Metrics.
We use error rates – defined as the proportion of incorrect predictions – to evaluate attack effectiveness. Given that the model poisoning attacks discussed are all untargeted, higher error rates indicate more effective attacks. To assess the stealthiness of our method in delivering malicious updates, we employ two metrics: 1) cosine similarity score: measures alignment with the server’s model update in FLTrust; 2) distance score: used in Multi-Krum to evaluate the closeness of poisoned updates to benign updates.

**TABLE IV: Error rates under cross-silo setting using “approximate max pill search” (10% malicious clients) on Fashion-MNIST dataset.**
| Data Distribution | IID | Non-IID |  |  |  |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Attack | FedAvg | FLTrust | MKrum | Bulyan | Median | Trim | FLD | FedAvg | FLTrust | MKrum | Bulyan | Median | Trim | FLD |
| No Attack | 0.106 | 0.104 | 0.103 | 0.108 | 0.127 | 0.107 | 0.116 | 0.111 | 0.119 | 0.113 | 0.113 | 0.140 | 0.114 | 0.123 |
| $\pm$ 0.003 | $\pm$0.003 | $\pm$0.003 | $\pm$0.004 | $\pm$0.001 | $\pm$0.002 | $\pm$0.002 | $\pm$0.002 | $\pm$0.003 | $\pm$0.001 | $\pm$0.002 | $\pm$0.005 | $\pm$0.002 | $\pm$0.004 |  |
| Sign-flipping Attack | 0.964 | 0.109 | 0.108 | 0.110 | 0.130 | 0.108 | 0.117 | 0.909 | 0.119 | 0.114 | 0.119 | 0.144 | 0.120 | 0.125 |
| $\pm$0.017 | $\pm$0.003 | $\pm$0.003 | $\pm$0.003 | $\pm$0.005 | $\pm$0.001 | $\pm$0.005 | $\pm$0.045 | $\pm$0.002 | $\pm$0.003 | $\pm$0.002 | $\pm$0.004 | $\pm$0.004 | $\pm$0.002 |  |
|  | 0.320 | 0.116 | 0.162 | 0.151 | 0.323 | 0.148 | 0.699 | 0.269 | 0.120 | 0.239 | 0.164 | 0.364 | 0.168 | 0.242 |
| + Poison Pill | $\pm$0.080 | $\pm$0.007 | $\pm$0.027 | $\pm$0.010 | $\pm$0.029 | $\pm$0.007 | $\pm$0.082 | $\pm$0.174 | $\pm$0.003 | $\pm$0.101 | $\pm$0.013 | $\pm$0.031 | $\pm$0.012 | $\pm$0.198 |
| Trim Attack | 0.112 | 0.111 | 0.111 | 0.115 | 0.132 | 0.114 | 0.116 | 0.125 | 0.115 | 0.121 | 0.125 | 0.153 | 0.122 | 0.122 |
| $\pm$0.002 | $\pm$0.005 | $\pm$0.004 | $\pm$0.003 | $\pm$0.004 | $\pm$0.003 | $\pm$0.001 | $\pm$0.003 | $\pm$0.006 | $\pm$0.001 | $\pm$0.004 | $\pm$0.005 | $\pm$0.002 | $\pm$0.002 |  |
|  | 0.508 | 0.139 | 0.334 | 0.126 | 0.284 | 0.127 | 0.120 | 0.528 | 0.148 | 0.455 | 0.143 | 0.287 | 0.146 | 0.136 |
| + Poison Pill | $\pm$0.128 | $\pm$0.012 | $\pm$0.120 | $\pm$0.006 | $\pm$0.040 | $\pm$0.004 | $\pm$0.003 | $\pm$0.051 | $\pm$0.018 | $\pm$0.151 | $\pm$0.003 | $\pm$0.023 | $\pm$0.004 | $\pm$0.012 |
| Krum Attack | 0.107 | 0.108 | 0.114 | 0.123 | 0.141 | 0.112 | 0.668 | 0.116 | 0.117 | 0.124 | 0.138 | 0.173 | 0.122 | 0.410 |
| $\pm$0.004 | $\pm$0.005 | $\pm$0.001 | $\pm$0.002 | $\pm$0.004 | $\pm$0.004 | $\pm$0.134 | $\pm$0.003 | $\pm$0.003 | $\pm$0.003 | $\pm$0.001 | $\pm$0.005 | $\pm$0.003 | $\pm$0.352 |  |
|  | 0.183 | 0.118 | 0.283 | 0.161 | 0.362 | 0.146 | 0.631 | 0.428 | 0.127 | 0.280 | 0.187 | 0.415 | 0.182 | 0.704 |
| + Poison Pill | $\pm$0.039 | $\pm$0.003 | $\pm$0.210 | $\pm$0.015 | $\pm$0.027 | $\pm$0.008 | $\pm$0.089 | $\pm$0.190 | $\pm$0.005 | $\pm$0.064 | $\pm$0.012 | $\pm$0.057 | $\pm$0.009 | $\pm$0.091 |
| Min-Max Attack | 0.117 | 0.108 | 0.118 | 0.135 | 0.142 | 0.128 | 0.111 | 0.124 | 0.119 | 0.142 | 0.166 | 0.162 | 0.145 | 0.136 |
| $\pm$0.004 | $\pm$0.004 | $\pm$0.002 | $\pm$0.005 | $\pm$0.009 | $\pm$0.008 | $\pm$0.004 | $\pm$0.003 | $\pm$0.012 | $\pm$0.003 | $\pm$0.007 | $\pm$0.004 | $\pm$0.004 | $\pm$0.002 |  |
|  | 0.439 | 0.129 | 0.361 | 0.136 | 0.343 | 0.150 | 0.715 | 0.521 | 0.136 | 0.339 | 0.153 | 0.368 | 0.184 | 0.335 |
| + Poison Pill | $\pm$0.140 | $\pm$0.009 | $\pm$0.245 | $\pm$0.015 | $\pm$0.032 | $\pm$0.006 | $\pm$0.096 | $\pm$0.073 | $\pm$0.011 | $\pm$0.202 | $\pm$0.009 | $\pm$0.048 | $\pm$0.017 | $\pm$0.185 |

Figure: Figure 5: Comparison of Multi-Krum distance score between benign updates and malicious updates when using original poisoning attacks with and without our method.
Refer to caption: x5.png

**TABLE V: Error rates under cross-silo setting using “approximate max pill search” (20% malicious clients) on CIFAR-10 dataset.**
| Data Distribution | IID | Non-IID |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Attack | FedAvg | FLTrust | MKrum | Bulyan | Median | Trim | DnC | FLD | Flame | FedAvg | FLTrust | MKrum | Bulyan | Median | Trim | DnC | FLD | Flame |
| No Attack | 0.488 | 0.480 | 0.507 | 0.469 | 0.551 | 0.456 | 0.445 | 0.494 | 0.491 | 0.486 | 0.474 | 0.499 | 0.498 | 0.581 | 0.502 | 0.463 | 0.506 | 0.532 |
| Sign-flipping Attack | 0.898 | 0.479 | 0.580 | 0.539 | 0.621 | 0.461 | 0.468 | 0.497 | 0.509 | 0.905 | 0.514 | 0.511 | 0.622 | 0.658 | 0.573 | 0.502 | 0.603 | 0.533 |
| + Poison Pill | 0.739 | 0.880 | 0.929 | 0.694 | 0.707 | 0.699 | 0.536 | 0.899 | 0.706 | 0.879 | 0.861 | 0.898 | 0.677 | 0.766 | 0.688 | 0.566 | 0.900 | 0.675 |
| Trim Attack | 0.482 | 0.509 | 0.489 | 0.536 | 0.623 | 0.514 | 0.456 | 0.459 | 0.501 | 0.571 | 0.493 | 0.608 | 0.595 | 0.653 | 0.549 | 0.481 | 0.482 | 0.506 |
| + Poison Pill | 0.853 | 0.877 | 0.883 | 0.654 | 0.674 | 0.662 | 0.513 | 0.899 | 0.542 | 0.890 | 0.862 | 0.906 | 0.772 | 0.688 | 0.639 | 0.518 | 0.893 | 0.621 |
| Krum Attack | 0.473 | 0.541 | 0.471 | 0.568 | 0.540 | 0.510 | 0.455 | 0.802 | 0.500 | 0.485 | 0.506 | 0.497 | 0.522 | 0.647 | 0.519 | 0.481 | 0.899 | 0.501 |
| + Poison Pill | 0.701 | 0.896 | 0.900 | 0.765 | 0.756 | 0.643 | 0.529 | 0.890 | 0.872 | 0.724 | 0.849 | 0.900 | 0.675 | 0.748 | 0.647 | 0.580 | 0.885 | 0.873 |
| Min-Max Attack | 0.450 | 0.504 | 0.469 | 0.507 | 0.579 | 0.465 | 0.514 | 0.525 | 0.525 | 0.478 | 0.502 | 0.493 | 0.568 | 0.636 | 0.603 | 0.478 | 0.482 | 0.488 |
| + Poison Pill | 0.752 | 0.712 | 0.902 | 0.775 | 0.802 | 0.640 | 0.545 | 0.902 | 0.811 | 0.661 | 0.646 | 0.886 | 0.674 | 0.783 | 0.661 | 0.527 | 0.907 | 0.799 |

Figure: Figure 6: Comparison of cosine similarity scores between original attack with and without our method under FLTrust.
Refer to caption: x6.png

Figure: Figure 7: Comparison of the cosine similarity scores with the server model of the original attack and our different augmentation patterns in the entire training period under FLTrust.
Refer to caption: x7.png

Figure: Figure 8: Comparison of error rates between original poisoning attacks and our method with two different pill search methods.
Refer to caption: x8.png

### 6.2 Augmentation Effectiveness

In this section, we present a comprehensive analysis of our method’s augmentation effectiveness on Fashion-MNIST dataset within a 50-client cross-silo FL system, in which 20% clients are malicious. We evaluate our method on both IID data and non-IID data. Our method successfully augments all the baseline attacks with more than $0.25$ average error rate increase, showing our method’s effectiveness and high compatibility.

Results on IID Data.
The error rates of four baseline FL poisoning attacks, with and without our method, are shown in the left half of Table III. Our method enhances the error rates of the existing poisoning attacks in $23$ out of $28$ scenarios, against FedAvg and five defenses. The maximum increase in error rate is $0.658$, and the average increase reaches $0.274$. This substantial elevation from the attack-free baseline error rate of $0.109$ underscores our method’s capability to significantly compromise existing defenses’ integrity. Following are individual improvements of our method on different baseline attacks:

- •
Sign-flipping attack: Its original version achieves a high error rate due to its aggressive and brute design, but it is effective only under FedAvg. Our method extends its impact to five more defenses (Multi-Krum, Bulyan, Median, Trim, and FLD), raising the average error rate by $0.399$.
- •
Trim and Krum attack: Our method enables these two attacks to successfully penetrate all baseline defenses (except for Trim attack against FLD) including FLTrust, which were previously unbreachable, with average error rate increases of $0.249$ and $0.253$, respectively.
- •
Min-Max attack: With our method, the Min-Max Attack shows a comprehensive improvement against all defenses except for a slight decrease against Bulyan, achieving an overall average error rate increase of $0.222$.

Results on Non-IID Data.
Evaluations on non-IID data further validate the effectiveness of our method, demonstrating its superiority in $23$ out of $28$ cases. The highest error rate increase reaches $0.637$, with an average increase of $0.281$. Although there is a slight reduction in maximal error rate increase in the non-IID data setting, these results still demonstrate our method’s ability to effectively enhance attacks in more complex and heterogeneous data environments.
The detailed improvements for a specific attack are shown as follows:

- •
Sign-flipping attack: Our method helps the sign-flipping attack achieve an average error rate increase of $0.404$, which is similar to the error rate increase on IID data.
- •
Trim and Krum attack: Both attacks penetrate all baseline defenses under the enhancement of our method, with average improvements of $0.281$ and $0.236$, respectively.
- •
Min-Max attack: Our method helps the Min-Max attack achieve an average error rate increase of $0.195$, higher than its original version. Although this error rate increase is lower than that in the IID data setting, it remains higher than the error rate increase caused by its original version.

All attacks augmented by our method can bypass all baseline defenses, including FLTrust and FLDetector, with the exception of the sign-flipping attack. Notably, the Min-Max attack demonstrates superior effectiveness in non-IID data settings, achieving significant improvements compared to its performance on IID data. Other attacks also exhibit similar error rate improvements relative to their results on IID data, indicating that our method maintains its robustness and effectiveness in more complex data environments.

### 6.3 Stealthiness Analysis

To further analyze the performance of our method, we analyze its stealthiness during the training process of the FL system, focusing on how our method influences the distance scores and cosine similarity scores of exisitng FL poisoning attacks. The results indicate that our method can make malicious clients appear as benign or even more “benign” than genuine benign clients. This significant increase in stealthiness is a result of the pill design with distance-based adjustment and similarity-based adjustment techniques in our method.

Distance Score Analysis.
Figure 5 compares the average distance scores of benign and malicious clients (with and without our method) across four baseline model poisoning attacks. The distance scores when using our method closely match or are even identical to those of benign clients throughout the entire training process. In contrast, original attacks like the Trim and sign-flipping attacks display distance scores that were significantly higher or lower than those of benign updates, indicating either detected by Multi-Krum (higher scores) or underutilized attack capacities (lower scores). Our method also has lower distance score variance in the early FL training period, representing that our method provides more steady attack efficacy in the FL’s critical training period [, ] by fully utilizing the attack capacities while being undetected. More details are shown in Appendix D.

Cosine Similarity Score Analysis.
Figure 6 shows that the angles between server model updates and malicious updates using our method are similar or even smaller than those of benign updates, leading to higher aggregation weights for malicious updates in FLTrust – illustrating why our method makes existing FL poisoning attacks effectively bypass FLTrust. In contrast, the angles between the FLTrust’s server model updates with original malicious updates are often greater than $90^{\circ}$, leading to a zero aggregation weight. Detailed per-round cosine similarity trends (Figure 7) also reveal that while original attacks often result in negative similarities (and thus are excluded by FLTrust), our method maintains positive similarities throughout the entire training process. This consistency not only ensures the successful insertion of the pill in any specific round but also secures pill’s long-lasting presence in the global model.

### 6.4 Generality Analysis

In this section, we further discuss the generality of our method from four perspectives: malicious client proportion, client participation frequency, datasets & model architectures, and pill search algorithm. The results indicate that our method maintains its augmentation effectiveness consistently, even as these conditions change, demonstrating its reliability and wide applicability in augmenting FL poisoning attacks.

Impact of The Malicious Client’s Proportion.
We first assess the effectiveness of our method in both IID and non-IID cross-silo FL system with only $10$% of clients compromised, as shown in Table IV. This setup revealed that all baseline model poisoning attacks yield lower error rates on the global model compared with scenarios with $20$% compromised clients. While the increase in error rates was less than those in the $20$% compromised client scenario, our method still effectively raises the global model’s error rates in $25$/$26$ out of $28$ cases (IID/non-IID setting). The maximum increase in error rates reached $0.403$, with an average increase of $0.144$. This average is notably higher (>$2$x higher) than the error rates observed in attack-free FL conditions. Specifically, our method help sign-flipping/Trim/Krum/Min-Max attacks achieve an average error rate increase of $0.133$/$0.094$/$0.136$/$0.209$. More detailed results are presented in Appendix G.

Impact of The Client Participation Frequency.
We then extend the evaluation of our method to a cross-device FL system, where only $40$% of clients are selected for participation in each communication round. This setup results in less frequent participation from each client and a fluctuating proportion of malicious clients across different rounds. The maximum error rate increase with our method is $0.639$, with an average increase across different attacks and defenses of $0.279$. These results are consistent with those from the cross-silo FL system, underscoring our method’s effectiveness and generality across different FL configurations. This evaluation demonstrates our method’s robust performance and adaptability, not only in a controlled cross-silo environment but also under the more various conditions in cross-device FL systems. More details are presented in Appendix F.

Impact of The Datasets and Model Architectures.
Following the evaluation with the Fashion-MNIST dataset, we test our method on the MNIST and CIFAR-10 dataset, employing the four-layer CNN model and the AlexNet model to further verify our method’s generality across different datasets.
The collective results show that our method performs even better with larger datasets or more complex machine learning models. This trend confirms the generality of our method by revealing its capability to maintain consistent performance enhancements regardless of the dataset or model complexity involved. Specifically, our method help all the four baseline attacks bypass all the nine baselines on CIFAR-10 dataset, achieving $0.288$ error rate increase on average, presented in Table V. More detailed results on MNIST dataset are shown in Appendix E.

Impact of The Pill Search Algorithm.
We conduct a final evaluation to assess the importance and effectiveness of the "approximate max pill search" algorithm used in our method. This is contrasted against a newly devised "approximate min pill search" algorithm, which targets the least important parameters within the target model. Figure 8 illustrates the error rates achieved by the "approximate max pill search", the "approximate min pill search", and the original model poisoning attacks. The "approximate max pill search" algorithm outperforms the "approximate min pill search" in $41$ out of $56$ cases (approximately $73$%), underscoring its effectiveness in leveraging the most influential parameters to enhance attack impacts. Despite its lower efficacy, the "approximate min pill search" still manage to surpass the original attacks in $41$ out of $56$ cases (approximately $73$%). This demonstrates the generality of our method across different pill search algorithms.

## 7 Discussion

### 7.1 Our Method against Possible Adaptive Defense

To further evaluate the robustness of our method when defenses are aware of the attack strategies (white-box scenario), we develop an adaptive defense named DSTrust, which enhances the FLTrust’s mechanism. DSTrust incorporates both distance and cosine similarity scores into a unified trust score calculation, directly countering our method’s two-step adjustment approach. The round-$t$ trust score of client $i$ in DSTrust is calculated as follows:

$$ $TS_{i}=ReLU(\frac{\mathop{cos}(\Delta\boldsymbol{g}_{t}^{(i)},\Delta \boldsymbol{g}_{t}^{s})}{||\Delta\boldsymbol{g}_{t}^{(i)}-\Delta\boldsymbol{g} _{t}^{s}||}),$ (3) $$

where $\Delta\boldsymbol{g}_{t}^{(i)}$ represents the model update from client $i$ and $\Delta\boldsymbol{g}_{t}^{s})$ represents server’s model update. By integrating both cosine similarity and distance metrics, DSTrust provides a more comprehensive defense approach compared with FLTrust. This dual consideration allows DSTrust to effectively mitigate attacks that manipulate either of these metrics to bypass defenses.

**TABLE VI: Error rates under cross-silo setting against the new adaptive defense – DSTrust – with and without our method on Fashion-MNIST dataset (20% malicious clients).**
| Data Distribution | IID | Non-IID |  |  |
| --- | --- | --- | --- | --- |
| Attack ($\mathcal{A}$) | \Centerstackw/o |  |  |  |
| Poison Pill | \Centerstackw/ |  |  |  |
| Poison Pill | \Centerstackw/o |  |  |  |
| Poison Pill | \Centerstackw/ |  |  |  |
| Poison Pill |  |  |  |  |
| No Attack | 0.108 | 0.116 |  |  |
| \CenterstackSign-flipping |  |  |  |  |
| Attack | 0.111 | 0.129 | 0.110 | 0.131 |
| Trim Attack | 0.109 | 0.629 | 0.115 | 0.630 |
| Krum Attack | 0.111 | 0.140 | 0.120 | 0.128 |
| Min-Max Attack | 0.127 | 0.167 | 0.143 | 0.327 |

Table VI details the error rates for four baseline FL poisoning attacks both with and without our method against the DSTrust defense on the Fashion-MNIST dataset within a 50-client FL system, where 20% of clients are malicious. These tests were conducted under both IID and non-IID data environments. DSTrust effectively neutralizes the four baseline poisoning attacks when our method is not applied, highlighting its robustness as a defense mechanism. Despite DSTrust’s integration of both cosine similarity and distance metrics in its defense strategy, it fails to counteract the augmented attacks when our method is employed. Notably, our method achieves a maximum error rate increase of $0.521$, and an average error rate increase of $0.173$ across all $8$ test scenarios. These results demonstrate that merely understanding the adjustment strategies of our method, and subsequently integrating corresponding defense metrics, does not fundamentally negate the effectiveness of our method. Despite the adaptive defense’s attempt to incorporate both cosine similarity and distance metrics into DSTrust, it remains insufficient to thwart the enhanced capabilities of our method.

### 7.2 Limitations and Future Work

Our method significantly enhances non-state-of-the-art (non-SOTA) model poisoning attacks, enabling them to SOTA results against various prevalent defenses. This is accomplished through a pill-based, attack-agnostic augmentation pipeline. We not only demonstrate our method’s capabilities but also expose fundamental vulnerabilities within the current designs of defense mechanisms.

For future attacks in FL, it is essential for attackers to meticulously evaluate the importance of each parameter in their implementation. By targeting specific subsets of parameters, attackers can devise more flexible and adaptive attacks, improving stealthiness and complicating defense efforts. As for future defenses, while individually checking each parameter might seem viable, its practical deployment is hindered by high overheads, making it infeasible in real-world applications.

Thus, there is a pressing need for more sophisticated defenses that can conduct fine-grained analyses of the roles of different parameters in neural networks, while executing without imposing prohibitive computational costs.

## 8 Conclusion

In this paper, we propose a novel attack-agnostic augmentation method to enhance existing poisoning attacks in FL by concentrating attacks into a pill (a tiny subnet). Our approach is constructed with three stages, including pill construction, pill poisoning, and pill injection. Accordingly, we first use a dynamic pill search algorithm to identify the pill blueprint. Then we poison the pill using existing FL poisoning attacks, and carefully inject the poison pill into the target model with two pill-related masks and a two-step adjustment. Our method enables existing poisoning attacks to achieve more than $2$x the error rates on average compared with their original implementations. The effectiveness of our method in exploiting and exacerbating the inherent weaknesses of current FL defenses highlights the critical need for more refined detection measures for FL.

## Appendix A Additional Details of The Baseline Defenses

Krum and Multi-Krum (MKrum) [].
Krum uses a distance score as the metric. In each round, the Krum server sums the distances between each client update $\boldsymbol{g}_{t}^{(i)}$ and its $K-m-2$ neighbors, and uses these sums as the scores for all the clients. The Krum server then selects the client’s model update with the lowest score. Multi-Krum is a variant of Krum that uses iterative Krum to pick multiple candidates for aggregation.

Coordinate-wise Median (Median) [].
Coordinate-wise Median (Median) uses the per-parameter median values of the model updates from the clients as the aggregated global model update, which is then used to generate the next-round global model.

Trimmed Mean (Trim) [].
Trimmed Mean (Trim) calculates per-parameter trimmed mean values of the client model updates and packs them as the global model update.

Bulyan [].
Bulyan is a combination of Krum and Trim. It first uses the Krum-based method to select multiple candidates, and uses the per-parameter trimmed mean values of the candidate model updates as the final global model update.

FLTrust [].
FLTrust trains a server model with a small root dataset. In each round, it computes the clipped cosine similarities between the server model update and client updates as trust scores, and then uses the trust scores as weights to aggregate all the normalized client model updates.

FLDetector (FLD) [].
FLDetector filters out malicious clients by checking the multi-round consistency of all client updates. Malicious updates typically have lower consistency compared to benign ones.

Flame [].
Flame utilizes HDBSCAN-based[] dynamic clustering to filtered out malicious clients, and aggregates median-clipped benign updates with adaptive noise as the global model update.

## Appendix B Additional Details of Concrete Pill Blueprints

Table VII and Table VIII illustrate the model structures of the CNN model and the simplified AlexNet, with their corresponding pill’s blueprints.

**TABLE VII: Architectures of the original CNN model and the corresponding pill blueprint.**
| Layer Type | \CenterstackOriginal |  |
| --- | --- | --- |
| CNN Model | \CenterstackOur |  |
| Pill Blueprint |  |  |
| Input | $28\times 28\times 1$ | $28\times 28\times 1$ |
| Conv2d | $3\times 3\times 30$ | $3\times 3\times 1$ |
| ReLU | -^1 | - |
| MaxPool2d | $2\times 2$ | $2\times 2$ |
| Conv2d | $3\times 3\times 50$ | $3\times 3\times 1$ |
| ReLU | - | - |
| MaxPool2d | $2\times 2$ | $2\times 2$ |
| Linear | $1250\times 100$ | $25\times 10$ |
| ReLU | - | - |
| Linaer | $100\times 10$ | $10$ |
| Softmax | - | $\times{\textsuperscript{2}}$ |

**TABLE VIII: Example architectures of original AlexNet and the corresponding pill blueprint.**
| Layer Type | \CenterstackOriginal |  |
| --- | --- | --- |
| AlexNet | \CenterstackOur |  |
| Pill Blueprint |  |  |
| Input | $32\times 32\times 3$ | $32\times 32\times 3$ |
| Conv2d | $11\times 11\times 48$ | $11\times 11\times 1$ |
| ReLU | - | - |
| MaxPool2d | $3\times 3$ | $3\times 3$ |
| Conv2d | $3\times 3\times 96$ | $3\times 3\times 1$ |
| ReLU | - | - |
| MaxPool2d | $3\times 3$ | $3\times 3$ |
| Conv2d | $3\times 3\times 192$ | $3\times 3\times 1$ |
| ReLU | - | - |
| Conv2d | $3\times 3\times 192$ | $3\times 3\times 1$ |
| ReLU | - | - |
| Conv2d | $3\times 3\times 128$ | $3\times 3\times 1$ |
| ReLU | - | - |
| MaxPool2d | $3\times 3$ | $3\times 3$ |
| Linear | $4608\times 1024$ | $36\times 1$ |
| ReLU | - | - |
| Linear | $1024\times 512$ | $1\times 10$ |
| ReLU | - | - |
| Linear | $512\times 10$ | $10$ |
| Softmax | - | $\times$ |

## Appendix C Additional Details of The Dynamic Patterns in Our Method

We first design three searching strategies, including one-time searching strategy, repeated searching strategy, and adaptive searching strategy.

In the one-time searching strategy, we search the pill based on the initial global model using the “approximate max pill search” algorithm introduced in §5.2, and keep this pill unchanged in the whole FL training. The one-time searching strategy benefits the formation of pill in the global model, while the initial pill may be less effective with the increasing training rounds of the global model, due to the changing importance of the model parameters.

On the contrary, the repeated searching strategy runs the ‘approximate max pill search” algorithm in every training round. The repeated searching strategy can help our method modify more parameters in the global model, and make the pill less traceable. While the attacking effects may be reduced due to the constantly changing pill.

Considering advantages and disadvantages of both the one-time searching strategy and the repeated searching strategy, we design a more flexible searching strategy, termed as “adaptive searching strategy”. In adaptive searching strategy, our method searches the new pill only when the pill is not successfully injected into the global model in the last round. The condition:

$$ $\texttt{Sim(}\boldsymbol{M}\odot\Delta\boldsymbol{g}_{t},\boldsymbol{M}\odot \Delta\boldsymbol{g}_{t}^{(i)}\texttt{)}<C_{search}$ (4) $$

should be satisfied to trigger the new subnetwork searching on malicious client $i$, where $C_{search}$ is set as $0.94$ in the experiments. The adaptive searching strategy is a mroe moderate version of repeated searching.

Since the three searching strategies have their unique advantages, we investigate different combinations of them in the experiments. We further divide the neural network into Feature Extractor (FE) and Classifier (CLS). Refer to the CNN model we used, the convolutional layers are regarded as FE, and the linear layers are regarded as CLS. We use different searching strategies in FE and CLS, respectively. In all the nine combinations, we test and keep six of them, noted as Pattern1 to Pattern6, shown in Table II in § 5.2. Such six patterns construct the entire dynamic pattern set used in our method.

**TABLE IX: Error rates under cross-silo setting using “approximate max pill search” (20% malicious clients) on MNIST dataset.**
| Data Distribution | IID | Non-IID |  |  |  |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Attack | FedAvg | FLTrust | MKrum | Bulyan | Median | Trim | FLD | FedAvg | FLTrust | MKrum | Bulyan | Median | Trim | FLD |
| No Attack | 0.028 | 0.051 | 0.029 | 0.029 | 0.045 | 0.029 | 0.025 | 0.029 | 0.042 | 0.030 | 0.029 | 0.041 | 0.029 | 0.022 |
| Sign-flipping Attack | 0.934 | 0.059 | 0.038 | 0.055 | 0.055 | 0.036 | 0.025 | 0.886 | 0.073 | 0.041 | 0.052 | 0.059 | 0.041 | 0.026 |
| + Poison Pill | 0.353 | 0.093 | 0.454 | 0.283 | 0.268 | 0.173 | 0.588 | 0.431 | 0.059 | 0.605 | 0.349 | 0.333 | 0.217 | 0.713 |
| Trim Attack | 0.257 | 0.065 | 0.182 | 0.103 | 0.106 | 0.123 | 0.022 | 0.418 | 0.059 | 0.295 | 0.209 | 0.245 | 0.310 | 0.021 |
| + Poison Pill | 0.416 | 0.109 | 0.469 | 0.252 | 0.247 | 0.117 | 0.026 | 0.581 | 0.065 | 0.672 | 0.358 | 0.324 | 0.092 | 0.051 |
| Krum Attack | 0.033 | 0.061 | 0.067 | 0.154 | 0.188 | 0.043 | 0.759 | 0.034 | 0.058 | 0.130 | 0.297 | 0.191 | 0.052 | 0.908 |
| + Poison Pill | 0.326 | 0.082 | 0.585 | 0.266 | 0.272 | 0.169 | 0.632 | 0.528 | 0.062 | 0.556 | 0.350 | 0.321 | 0.210 | 0.746 |
| Min-Max Attack | 0.307 | 0.082 | 0.693 | 0.731 | 0.341 | 0.255 | 0.915 | 0.359 | 0.161 | 0.718 | 0.993 | 0.381 | 0.320 | 0.853 |
| + Poison Pill | 0.402 | 0.106 | 0.518 | 0.273 | 0.262 | 0.218 | 0.766 | 0.534 | 0.077 | 0.707 | 0.369 | 0.318 | 0.194 | 0.861 |

**TABLE X: Error rates under cross-device setting using “approximate max pill search” (20% malicious clients) on Fashion-MNIST dataset in both IID and non-IID data distribution.**
| Data Distribution | IID | Non-IID |  |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Attack | FedAvg | FLTrust | MKrum | Bulyan | Median | Trim | FedAvg | FLTrust | MKrum | Bulyan | Median | Trim |
| No Attack | 0.107 | 0.111 | 0.108 | 0.105 | 0.138 | 0.106 | 0.113 | 0.124 | 0.115 | 0.118 | 0.164 | 0.116 |
| $\pm$0.004 | $\pm$0.003 | $\pm$0.003 | $\pm$0.003 | $\pm$0.010 | $\pm$0.003 | $\pm$0.002 | $\pm$0.008 | $\pm$0.006 | $\pm$0.002 | $\pm$0.009 | $\pm$0.005 |  |
| Sign-flipping Attack | 0.940 | 0.116 | 0.110 | 0.128 | 0.165 | 0.121 | 0.905 | 0.124 | 0.118 | 0.136 | 0.184 | 0.134 |
| $\pm$0.026 | $\pm$0.003 | $\pm$0.003 | $\pm$0.005 | $\pm$0.007 | $\pm$0.004 | $\pm$0.031 | $\pm$0.004 | $\pm$0.002 | $\pm$0.003 | $\pm$0.007 | $\pm$0.006 |  |
|  | 0.591 | 0.117 | 0.749 | 0.357 | 0.589 | 0.225 | 0.573 | 0.125 | 0.665 | 0.379 | 0.662 | 0.277 |
| + Poison Pill | $\pm$0.177 | $\pm$0.004 | $\pm$0.076 | $\pm$0.057 | $\pm$0.048 | $\pm$0.026 | $\pm$0.140 | $\pm$0.004 | $\pm$0.111 | $\pm$0.018 | $\pm$0.131 | $\pm$0.012 |
| Trim Attack | 0.240 | 0.110 | 0.151 | 0.148 | 0.207 | 0.178 | 0.340 | 0.120 | 0.228 | 0.190 | 0.237 | 0.245 |
| $\pm$0.018 | $\pm$0.004 | $\pm$0.010 | $\pm$0.002 | $\pm$0.014 | $\pm$0.004 | $\pm$0.048 | $\pm$0.002 | $\pm$0.025 | $\pm$0.016 | $\pm$0.016 | $\pm$0.011 |  |
|  | 0.620 | 0.492 | 0.620 | 0.228 | 0.424 | 0.232 | 0.654 | 0.533 | 0.679 | 0.324 | 0.483 | 0.226 |
| + Poison Pill | $\pm$0.051 | $\pm$0.023 | $\pm$0.025 | $\pm$0.025 | $\pm$0.042 | $\pm$0.035 | $\pm$0.037 | $\pm$0.041 | $\pm$0.049 | $\pm$0.098 | $\pm$0.098 | $\pm$0.025 |
| Krum Attack | 0.117 | 0.112 | 0.172 | 0.238 | 0.169 | 0.132 | 0.126 | 0.121 | 0.204 | 0.296 | 0.222 | 0.158 |
| $\pm$0.002 | $\pm$0.004 | $\pm$0.010 | $\pm$0.005 | $\pm$0.009 | $\pm$0.003 | $\pm$0.005 | $\pm$0.004 | $\pm$0.031 | $\pm$0.011 | $\pm$0.014 | $\pm$0.006 |  |
|  | 0.681 | 0.138 | 0.740 | 0.362 | 0.572 | 0.258 | 0.604 | 0.141 | 0.750 | 0.372 | 0.649 | 0.277 |
| + Poison Pill | $\pm$0.057 | $\pm$0.015 | $\pm$0.092 | $\pm$0.073 | $\pm$0.167 | $\pm$0.018 | $\pm$0.125 | $\pm$0.009 | $\pm$0.081 | $\pm$0.035 | $\pm$0.184 | $\pm$0.013 |
| Min-Max Attack | 0.146 | 0.111 | 0.382 | 0.324 | 0.183 | 0.185 | 0.191 | 0.147 | 0.621 | 0.426 | 0.245 | 0.279 |
| $\pm$0.005 | $\pm$0.002 | $\pm$0.036 | $\pm$0.012 | $\pm$0.011 | $\pm$0.006 | $\pm$0.014 | $\pm$0.024 | $\pm$0.112 | $\pm$0.072 | $\pm$0.013 | $\pm$0.007 |  |
|  | 0.651 | 0.244 | 0.718 | 0.312 | 0.503 | 0.249 | 0.670 | 0.229 | 0.621 | 0.349 | 0.581 | 0.386 |
| + Poison Pill | $\pm$0.082 | $\pm$0.104 | $\pm$0.059 | $\pm$0.026 | $\pm$0.060 | $\pm$0.014 | $\pm$0.123 | $\pm$0.098 | $\pm$0.030 | $\pm$0.047 | $\pm$0.161 | $\pm$0.141 |

**TABLE XI: Error rates under cross-device setting using “approximate max pill search” (10% malicious clients) on Fashion-MNIST dataset in both IID and non-IID data distribution.**
| Data Distribution | IID | Non-IID |  |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Attack | FedAvg | FLTrust | MKrum | Bulyan | Median | Trim | FedAvg | FLTrust | MKrum | Bulyan | Median | Trim |
| No Attack | 0.110 | 0.106 | 0.107 | 0.108 | 0.139 | 0.106 | 0.115 | 0.117 | 0.115 | 0.117 | 0.164 | 0.112 |
| $\pm$0.004 | $\pm$0.004 | $\pm$0.005 | $\pm$0.003 | $\pm$0.008 | $\pm$0.003 | $\pm$0.003 | $\pm$0.003 | $\pm$0.003 | $\pm$0.002 | $\pm$0.004 | $\pm$0.002 |  |
| Sign-flipping Attack | 0.929 | 0.111 | 0.108 | 0.111 | 0.153 | 0.117 | 0.902 | 0.118 | 0.115 | 0.120 | 0.175 | 0.134 |
| $\pm$0.026 | $\pm$0.004 | $\pm$0.003 | $\pm$0.002 | $\pm$0.025 | $\pm$0.004 | $\pm$0.034 | $\pm$0.001 | $\pm$0.004 | $\pm$0.003 | $\pm$0.008 | $\pm$0.008 |  |
|  | 0.195 | 0.114 | 0.170 | 0.138 | 0.347 | 0.137 | 0.330 | 0.124 | 0.165 | 0.148 | 0.483 | 0.161 |
| + Poison Pill | $\pm$0.032 | $\pm$0.003 | $\pm$0.071 | $\pm$0.005 | $\pm$0.059 | $\pm$0.004 | $\pm$0.135 | $\pm$0.006 | $\pm$0.016 | $\pm$0.007 | $\pm$0.225 | $\pm$0.009 |
| Trim Attack | 0.112 | 0.114 | 0.111 | 0.118 | 0.153 | 0.113 | 0.129 | 0.125 | 0.128 | 0.129 | 0.185 | 0.122 |
| $\pm$0.003 | $\pm$0.004 | $\pm$0.002 | $\pm$0.005 | $\pm$0.021 | $\pm$0.004 | $\pm$0.003 | $\pm$0.004 | $\pm$0.004 | $\pm$0.003 | $\pm$0.011 | $\pm$0.003 |  |
|  | 0.369 | 0.138 | 0.212 | 0.128 | 0.310 | 0.140 | 0.589 | 0.154 | 0.300 | 0.139 | 0.351 | 0.156 |
| + Poison Pill | $\pm$0.147 | $\pm$0.015 | $\pm$0.066 | $\pm$0.005 | $\pm$0.038 | $\pm$0.014 | $\pm$0.046 | $\pm$0.017 | $\pm$0.100 | $\pm$0.006 | $\pm$0.056 | $\pm$0.015 |
| Krum Attack | 0.110 | 0.113 | 0.115 | 0.128 | 0.144 | 0.113 | 0.121 | 0.116 | 0.123 | 0.135 | 0.183 | 0.120 |
| $\pm$0.003 | $\pm$0.001 | $\pm$0.003 | $\pm$0.004 | $\pm$0.004 | $\pm$0.003 | $\pm$0.002 | $\pm$0.001 | $\pm$0.004 | $\pm$0.004 | $\pm$0.008 | $\pm$0.001 |  |
|  | 0.164 | 0.117 | 0.157 | 0.143 | 0.371 | 0.142 | 0.229 | 0.126 | 0.249 | 0.146 | 0.374 | 0.157 |
| + Poison Pill | $\pm$0.038 | $\pm$0.003 | $\pm$0.044 | $\pm$0.010 | $\pm$0.034 | $\pm$0.005 | $\pm$0.069 | $\pm$0.002 | $\pm$0.167 | $\pm$0.004 | $\pm$0.023 | $\pm$0.005 |
| Min-Max Attack | 0.116 | 0.111 | 0.116 | 0.127 | 0.145 | 0.122 | 0.121 | 0.116 | 0.123 | 0.135 | 0.183 | 0.120 |
| $\pm$0.002 | $\pm$0.002 | $\pm$0.001 | $\pm$0.004 | $\pm$0.006 | $\pm$0.003 | $\pm$0.002 | $\pm$0.001 | $\pm$0.004 | $\pm$0.004 | $\pm$0.008 | $\pm$0.001 |  |
|  | 0.351 | 0.124 | 0.299 | 0.135 | 0.343 | 0.146 | 0.342 | 0.138 | 0.292 | 0.148 | 0.417 | 0.166 |
| + Poison Pill | $\pm$0.204 | $\pm$0.019 | $\pm$0.110 | $\pm$0.004 | $\pm$0.070 | $\pm$0.019 | $\pm$0.076 | $\pm$0.018 | $\pm$0.087 | $\pm$0.009 | $\pm$0.050 | $\pm$0.010 |

## Appendix D Additional Stealthiness Analysis

As shown in Figure 5, in addition to the minimal difference between the benign updates and the augmented malicious updates, our method also achieves two more improvements. First, our method causes the global model to degrade earlier compared to the original attacks, further demonstrating the effectiveness of our augmentation. Second, our method significantly increases the discrepancy between benign client updates as the communication rounds increase. While original attacks can bypass detection in some cases, the discrepancy between benign client updates remains steady, illustrating the lower impact of malicious clients. In contrast, our method consistently increases the discrepancy among benign clients, highlighting its penetrating effectiveness in its influence on benign clients’ local training.

## Appendix E Additonal Details on MNIST and CIFAR-10 Dataset

The detailed results on MNIST and CIFAR-10 dataset are presented in Table IX (MNIST dataset) and Table V (CIFAR-10 dataset), respectively.

For MNIST dataset, the highest error rate increase achieved using our method is $0.518$, with an average increase of $0.121$. This average error rate increase is slightly lower compared with the improvement observed on the Fashion-MNIST dataset. Despite the reduced average error rate increase, it remains significant, especially considering the MNIST dataset’s lower baseline error rates (below 0.070).

On CIFAR-10 dataset, our method help existing FL poisoning attacks outperform their original versions in $71$ of the $72$ scenarios, with an average error rate increase of over $0.288$. Specifically, our method facilitates at least a $0.212$ increase in error rates against FLTrust, outperforming the results in the same settings on the Fashion-MNIST dataset.

## Appendix F Additional Results in Cross-device FL System

After evaluating our method in the 50-client cross-silo FL system, we further test it in the 50-client cross-device FL system. Table X presents the error rates under the cross-device FL setting using the “approximate max pill search” algorithm on both IID and non-IID data. We report the highest error rates among the results of six dynamic patterns, with the malicious client proportion set to $20\%$. Since FLD is not typically designed for cross-device systems, we do not test it in this setting.

Results on IID Data. The highest error rate improvement with our method achieves $0.639$, and the average error rate increase with our method reaches $0.279$. With our method, existing model poisoning attacks outperform their original versions in $22$ out of the $24$ cases. The highest error rate improvement for the sign-flipping attack is $0.639$, with an average error rate increase of $0.279$. For the Trim attack and Krum attack, the highest error rate increases are $0.469$ and $0.568$, with average error rate increases of $0.264$ and $0.302$, respectively. For the Min-Max attack, the highest error rate increase reaches $0.505$, with an average increase of $0.272$. These improvements are consistent with the error rates observed under the cross-silo FL setting using the "approximate max pill search" algorithm on IID data.

Results on non-IID Data. As for the results on non-IID data, the highest error rate improvement with our method achieves $0.546$, and the average error rate increase with our method reaches $0.273$. By using our method, existing model poisoning attacks outperform their original versions in 21 out of the 24 cases. The highest error rate improvement for the sign-flipping attack is $0.547$, with an average error rate increase of $0.282$. For the Trim attack and Krum attack, the highest error rate rises are $0.451$ and $0.546$, with average error rate rises of $0.312$ and $0.278$, respectively. For the Min-Max attack, the highest error rate increase reaches $0.479$, with an average increase of $0.201$. These improvements are also aligned with the error rates observed under the cross-silo FL setting using the max subnetwork searching algorithm on non-IID data.

The average error rates of the global model in the cross-device FL system are lower than the error rates in the cross-silo FL system within $0.030$, illustrating our method’s generality over different data distribution and FL systems.

## Appendix G Additional Results with Fewer Malicious Clients

We also test the error rate improvement of our method in both the IID and non-IID cross-device FL systems, with only $10\%$ malicious clients. The experimental results are shown in Table XI.

Results on IID Data with Fewer Malicious Clients. The highest error rate increment is $0.257$, with an average increment of $0.083$. The error rate increments in the cross-device FL system are smaller than those in the cross-silo FL system, as malicious clients may not be selected in every round. However, this reduction in improvement is acceptable since our method helps existing model poisoning attacks outperform their original versions in $23$ out of $24$ cases. Furthermore, when all existing attacks fail to bypass any defenses with $10\%$ malicious clients, our method enables the attacks to bypass all defenses. The superiority of our method is maintained even with $10\%$ compromised clients.

Results on Non-IID Data with Fewer Malicious Clients. The results on the non-IID data are similar to those on the IID data. The highest error rate increment is $0.460$, and the average error rate increment is $0.079$. Our method helps existing model poisoning attacks achieve higher error rates in $23$ out of $24$ cases, even in highly unstable and heterogeneous settings. These results demonstrate the generality and robustness of our method across different data distributions and client selection methods with only a small portion of malicious clients.

## Peer Reviews

confidence: 5
contribution: 2
initial_rating: 3
presentation: 2
questions: 1. This method seems over-complicated with extensive training steps. How is the computational time of this attack compared to others from benign clients? whether it can introduce too long a waiting tie and raise suspicious from the server.
2. How to ensure the fidelity of the estimated benign update, since it is heavily depending on the quality and distribution of the data from the adversarial clients? What is the assumption about the data that the adversary has across compromised clients?
3. In Line 257,  what is the contribution of the starting point defined by an attacker on the overall performance since the error can be propagated to the next layers if the first neuron on the first layer is not the optimal one? This procedure and why does it work is unclear to me.
What is the indication of these fill’s neurons, and their role on the network?
This discussion should be presented in the main manuscript.
4. The number of clients is smaller than the existing FL attack setting, which may lead to a large amount of data that an attacker has, could authors show this method can work with larger systems and smaller amounts of data from attackers?
5. “Malicious 20%” is too strong and unrealistic, whereas practical attacks often use 1-5% of the malicious clients. Whether or not this attack can work with smaller attacker numbers?
6. Can this method be used with backdoor attacks?
7. Can this method work with more complex model architecture such as ResNet/VGG?
soundness: 2
strengths: - This method can be combined with various poisoning attacks in FL and the idea of using sub-network sounds interesting
- The experiments are conducted on nine defenses and three datasets and show some improvement in increasing error rates
- This paper considers the stealthiness and effectiveness of the attack at the same time
summary: This paper introduces an attack-agnostic augmentation method to bypass existing FL defenses and increase error rates. The high-level idea of this method is to constrain the poisoned updates into a sub-network instead of poisoning the whole model architecture. Via empirical experiments, this method is shown to bypass existing FL defenses and can be used as an add-on to four model poisoning attacks.
weaknesses: 1. The writing of this paper needs extensive revision for the following reasons: (i) the method is written using mostly oral sentences, lacking formal equations and (ii) the intuition and contribution of each sub-component are unclear. For example, in “**Designing Pill Blueprint”,** how the most important parameters are determined and what is its contribution to the overall method is not clearly stated.
2. This paper lacks an ablation study to investigate the effectiveness of each component including (i) the percentage of data that the adversary holds per total client data, (ii) the effect of SimAdjust and DisAdjust in L16-17, Algo. 1.
3. Results in Table 2 do not sound convincing. Except for Sign-flipping Attack, the error rates of baseline attacks are very low, i.e., 10-20% even with FedAvg (no-defense), and FLD defense even increases the error rates. On the other hand, in the original paper from Fang et al. 2020, the error rates can reach up to 80% in some cases. Can the authors explain these?
4. Related attacks focusing on model poisoning to increase stealthiness and effectiveness such as PDG, Model Replacement, Constrain-and-Scale, and Neurotoxin are not compared or discussed.

[1] Bagdasaryan, Eugene, et al. "How to backdoor federated learning." *International conference on artificial intelligence and statistics*. PMLR, 2020.

[2]  Bhagoji, Arjun Nitin, et al. "Analyzing federated learning through an adversarial lens." *International conference on machine learning*. PMLR, 2019.

[3] Zhang, Zhengming, et al. "Neurotoxin: Durable backdoors in federated learning." *International Conference on Machine Learning*. PMLR, 2022.

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confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: • The 'Dynamic Pill Search' component is key point of the proposed method, but its details are missing from the main content. It is recommended to move these details from the appendix into the main text.

• Regarding the 'Pill Poisoning' design, it utilizes an ‘extra-trained model update’ as a reference model, aiming to make the generated malicious model update less divergent from the global model. This seems to contradict the original attack's goal, which is to maximize the difference between malicious and benign models. Therefore, the reviewer questions why this approach would outperform the original attacks in the FedAvg case.

• It is also suggested to evaluate the proposed method on targeted attacks.
soundness: 3
strengths: • The manuscript is well-written and easy to follow.

• The study conducts extensive experiments to verify the effectiveness of the proposed method.
summary: This manuscript introduces an attack-agnostic augmentation technique designed to improve the stealth and effectiveness of existing poisoning attacks in Federated Learning (FL) systems. Experimental results demonstrate that, when enhanced by this method, FL poisoning attacks can evade state-of-the-art defenses, resulting in up to a 7-fold increase in error rates for current attack models.
weaknesses: • The technical challenges and gaps in existing works are not adequately addressed. From the reviewer's perspective, this study merely adds an additional component that aims to identify 'critical neurons' in the network and applies existing attacks only to those neurons. The reviewer does not see the benefit of this extra component, as it may weaken the attack's effectiveness and increase computational overhead due to the identification process.

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confidence: 3
contribution: 2
initial_rating: 5
presentation: 2
questions: See the weakness.
soundness: 2
strengths: The proposed method introduces a unique attack strategy that optimizes stealth by selectively poisoning critical model parameters, making it less detectable by standard FL defenses.
summary: This paper proposes a novel attack-agnostic augmentation method to enhance the stealthiness and efficacy of existing poisoning attacks in federated learning (FL) systems. Unlike traditional poisoning methods that modify all model parameters uniformly, the proposed method targets a compact subnetwork, or "pill," which allows for a more subtle attack capable of evading detection. The approach involves three main stages: pill construction, pill poisoning, and pill injection. By focusing on critical model parameters, this method bypasses multiple state-of-the-art (SOTA) defenses in FL, as evidenced by experimental results showing significantly increased error rates under various conditions.
weaknesses: The paper does not thoroughly address the scalability of the proposed attack in large-scale federated learning (FL) environments with diverse client devices and varying computational capabilities. 

The dynamic pill search and injection stages could introduce significant computational overhead, especially in deep or complex models. This overhead could limit the attack's practicality in scenarios with frequent model updates or large FL models. 

The paper’s experiments focus on a few specific model architectures, and it remains unclear whether the proposed pill-based poisoning technique can generalize across diverse FL model types, such as transformer-based models. 

More baseline attacks should be considered.

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confidence: 3
contribution: 3
initial_rating: 8
presentation: 3
questions: 1. How frequently do the compromised and benign clients update their models? Can the compromised clients send their update after some iterations where no poisoned update has been sent? 
2. In a scenario where the current benign model updates that will be sent are severely out of proportion compared to their old updates, will the attack be detected? Since the compromised client sent their model update, calibrated according to the magnitude of previous benign model updates?
3. How does the estimation process work for different aggregation methods used in federated learning? Is it always the mean of all the compromised model weights? This estimation procedure makes sense when the FedAvg aggregation rule is used, but it also seems to work for Median, FLTrust, and Flame. Can you prove your perspective on this?
4. In the pill search algorithm, how important is the initialization? What is the effect of different types of initialization?
soundness: 3
strengths: 1. Their method can incorporate both cross-silo and cross-device configurations.
2. Their method is general with respect to the poisoning attack they are augmenting.
3. Using their augmentation method, they can make non-SOTA poisoning attacks perform as well as SOTA model poisoning attacks.
4. In the appendix, they show that even 10% of compromised clients can evade detection when the other attacks fail.
5. Even with non-IID data, they outperform the existing attacks in 23 out of 24 cases and achieve high error rates even in unstable and heterogeneous environments. 
6. They even provided a defense against their method. It would be nice to have some evaluation regarding it.
summary: This paper is about model poisoning attacks by augmenting current poisoning attacks with efficient parameter update methodology. The author's critical observation is that not all model parameters contribute equally during inference. Therefore, if they can selectively modify the subset of parameters (critical parameters of pill) contributing, they will be able to make the attack more stealthy and ensure that it evades current defenses. They poison the pill using existing FL poisoning attacks, and carefully inject the poison pill into the target model. To raise suspicion, they ensure that the magnitude of parameter change is balanced with benign parameter's model update. They evaluated using 9 different aggregation rules, 4 model poisoning models and used well-known MNIST, Fashion-MNIST, and CIFAR-10 datasets to validate their claim. They also evaluated using both IID and non-IID data.
weaknesses: 1. Some details regarding the parameter update are missing, such as the frequency of the model updates and how they are scheduled.  Do the compromised and non-compromised models send their updates simultaneously? Also, do they send their updates at regular intervals or differently?
2. The parameter estimation process needs to be explained more in-depth concerning the other eight aggregation rules used in the paper. Can you give me an example of how it would work for Median, FLTrust, and Flame?
3. The threat model is somewhat weak since it assumes they completely control the compromised clients. Can you tell me how the error rate would change under this scenario, where, let's say, we start with 20% of the fleet as compromised, then after some time, the number of compromised fleets decreases to 15%, then 10%, and finally 5%?

Meta-review: This paper proposes a data augmentation technique called Poison Pill to help various backdoor attacks on federated learning (FL) evade existing defenses. By demonstrating its effectiveness across different scenarios, the work highlights the vulnerability of current FL systems to advanced poisoning strategies like the proposed one. Although one reviewer was very positive about the paper, other reviewers raised major concerns regarding the presentation, clarity of the proposed methodology, experimental setting, and the scale of the experiments. While the authors have addressed some of these concerns with new results, several issues remain unresolved. In its current form, the paper does not meet ICLR's expectations. I recommend the authors continue refining the paper for future submissions.

Decision: Reject