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Title: DBA: Distributed Backdoor Attacks against Federated Learning

Paper content:
Backdoor attacks aim to manipulate a subset of training data by injecting adversarial triggers such that machine learning models trained on the tampered dataset will make arbitrarily (targeted) incorrect prediction on the testset with the same trigger embedded.
While federated learning (FL) is capable of aggregating information provided by different parties for training a better model, its distributed learning methodology and inherently heterogeneous data distribution across parties may bring new vulnerabilities.
In addition to recent centralized backdoor attacks on FL where each party embeds the same global trigger during training, we propose the distributed backdoor attack (DBA) --- a novel threat assessment framework developed by fully exploiting the distributed nature of FL.
DBA decomposes a global trigger pattern into separate local patterns and embed them into the training set of different adversarial parties respectively.
Compared to standard centralized backdoors, we show that DBA is substantially more persistent and stealthy against FL on diverse datasets such as finance and image data.
We conduct extensive experiments to show that the attack success rate of DBA is significantly higher than centralized backdoors under different settings.
Moreover, we find that distributed attacks are indeed more insidious, as DBA can evade two state-of-the-art robust FL algorithms against centralized backdoors.
We also provide explanations for the effectiveness of DBA via feature visual interpretation and feature importance ranking.
To further explore the properties of DBA, we test the attack performance by varying different trigger factors, including local trigger variations (size, gap, and location), scaling factor in FL, data distribution, and poison ratio and interval.
Our proposed DBA and thorough evaluation results shed lights on characterizing the robustness of FL.
Federated learning (FL) has been recently proposed to address the problems for training machine learning models without direct access to diverse training data, especially for privacy-sensitive tasks (Smith et al., 2017; McMahan et al., 2017; Zhao et al., 2018) .
Utilizing local training data of participants (i.e., parties), FL helps train a shared global model with improved performance.
There have been prominent applications and ever-growing trends in deploying FL in practice, such as loan status prediction, health situation assessment (e.g. potential cancer risk assessment), and next-word prediction while typing (Hard et al., 2018; Yang et al., 2018; 2019) .
Although FL is capable of aggregating dispersed (and often restricted) information provided by different parties to train a better model, its distributed learning methodology as well as inherently heterogeneous (i.e., non-i.i.d.) data distribution across different parties may unintentionally provide a venue to new attacks.
In particular, the fact of limiting access to individual party's data due to privacy concerns or regulation constraints may facilitate backdoor attacks on the shared model trained with FL.
Backdoor attack is a type of data poisoning attacks that aim to manipulate a subset of training data such that machine learning models trained on the tampered dataset will be vulnerable to the test set with similar trigger embedded (Gu et al., 2019) .
Backdoor attacks on FL have been recently studied in (Bagdasaryan et al., 2018; Bhagoji et al., 2019) .
However, current attacks do not fully exploit the distributed learning methodology of FL, as they embed the same global trigger pattern to all adversarial parties.
We call such attacking scheme Figure 1: Overview of centralized and distributed backdoor attacks (DBA) on FL.
The aggregator at round t + 1 combines information from local parties (benign and adversarial) in the previous round t, and update the shared model G t+1 .
When implementing backdoor attacks, centralized attacker uses a global trigger while distributed attacker uses a local trigger which is part of the global one.
centralized backdoor attack.
Leveraging the power of FL in aggregating dispersed information from local parties to train a shared model, in this paper we propose distributed backdoor attack (DBA) against FL.
Given the same global trigger pattern as the centralized attack, DBA decomposes it into local patterns and embed them to different adversarial parties respectively.
A schematic comparison between the centralized and distributed backdoor attacks is illustrated in Fig.1 .
Through extensive experiments on several financial and image datasets and in-depth analysis, we summarize our main contributions and findings as follows.
• We propose a novel distributed backdoor attack strategy DBA on FL and show that DBA is more persistent and effective than centralized backdoor attack.
Based on extensive experiments, we report a prominent phenomenon that although each adversarial party is only implanted with a local trigger pattern via DBA, their assembled pattern (i.e., global trigger) attains significantly better attack performance on the global model compared with the centralized attack.
The results are consistent across datasets and under different attacking scenarios such as one-time (single-shot) and continuous (multiple-shot) poisoning settings.
To the best of our knowledge, this paper is the first work studying distributed backdoor attacks.
• When evaluating the robustness of two recent robust FL methods against centralized backdoor attack (Fung et al., 2018; Pillutla et al., 2019) , we find that DBA is more effective and stealthy, as its local trigger pattern is more insidious and hence easier to bypass the robust aggregation rules.
• We provide in-depth explanations for the effectiveness of DBA from different perspectives, including feature visual interpretation and feature importance ranking.
• We perform comprehensive analysis and ablation studies on several trigger factors in DBA, including the size, gap, and location of local triggers, scaling effect in FL, poisoning interval, data poisoning ratio, and data distribution.
Specifically, at round t, the central server sends the current shared model G t to n ∈ [N ] selected parties, where [N ] denotes the integer set {1, 2, . . . , N }.
The selected party i locally computes the function f i by running an optimization algorithm such as stochastic gradient descent (SGD) for E local epochs with its own dataset D i and learning rate l r to obtain a new local model L t+1 i
. The local party then sends model update L t+1 i − G t back to the central server, who will averages over all updates with its own learning rate η to generate a new global model G t+1 :
This aggregation process will be iterated until FL finds the final global model.
Unless specified otherwise, we use G t (L t i ) to denote the model parameters of the global (local) model at round t.
Attacker ability.
Based on the Kerckhoffs's theory (Shannon, 1949) , we consider the strong attacker here who has full control of their local training process, such as backdoor data injection and updating local training hyperparameters including E and l r .
This scenario is quite practical since each local dataset is usually owned by one of the local parties.
However, attackers do not have the ability to influence the privilege of central server such as changing aggregation rules, nor tampering the training process and model updates of other parties.
Objective of backdoor attack.
Backdoor attack is designed to mislead the trained model to predict a target label τ on any input data that has an attacker-chosen pattern (i.e., a trigger) embedded.
Instead of preventing the convergence in accuracy as Byzantine attacks (Blanchard et al., 2017) , the purpose of backdoor attacks in FL is to manipulate local models and simultaneously fit the main task and backdoor task, so that the global model would behave normally on untampered data samples while achieving high attack success rate on backdoored data samples.
The adversarial objective for attacker i in round t with local datatset D i and target label τ is:
Here, the poisoned dataset
The function R transforms clean data in any class into backdoored data that have an attacker-chosen trigger pattern using a set of parameters φ.
For example, for image data, φ is factored into trigger location TL, trigger size TS and trigger gap TG (φ = {TS, TG, TL}), which are shown in Fig.2 .
The attacker can design his own trigger pattern and choose an optimal poison ratio r to result in a better model parameter w * i , with which G t+1 can both assign the highest probability to target label τ for backdoored data R(x i j , φ) and the ground truth label y i j for benign data x i j .
Through extensive experiments on diverse datasets including LOAN and three image datasets in different settings, we show that in standard FL our proposed DBA is more persistent and effective than centralized backdoor attack: DBA achieves higher attack success rate, faster convergence and better resiliency in single-shot and multiple-shot attack scenarios.
We also demonstrate that DBA is more stealthy and can successfully evade two robust FL approaches.
The effectiveness of DBA is explained using feature visual interpretation for inspecting its role in aggregation.
We also perform an in-depth analysis on the important factors that are unique to DBA to explore its properties and limitations.
Our results suggest DBA is a new and more powerful attack on FL than current backdoor attacks.
Our analysis and findings can provide new threat assessment tools and novel insights for evaluating the adversarial robustness of FL.
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assistant: text: We proposed a novel distributed backdoor attack on federated learning and show that it is not only more effective compared with standard centralized attacks, but also harder to be defended by existing robust FL methods