Original: CoPur: Certifiably Robust Collaborative Inference via Feature Purification
Revised: CoPur: Certifiably Robust Collaborative Inference via Feature Purification

Original: Anonymous Author(s)
Revised: Anonymous Author(s)

Original: AffiliationAddress email
Revised: AffiliationAddress email

Original: Abstract
Revised: Abstract

Original: Collaborative inference leverages diverse features provided by different agents(e.g., sensors) for more accurate inference.
Revised: Collaborative inference leverages diverse features provided by different agents(e.g., sensors) for more accurate inference.

Original: A common setup is where each agentsends its embedded features instead of the raw data to the Fusion Center (FC)for joint prediction.
Revised: A common setup is where each agentsends its embedded features instead of the raw data to the Fusion Center (FC)for joint prediction.

Original: In this setting, we consider inference phase attacks when asmall fraction of agents are compromised.
Revised: In this setting, we consider inference phase attacks when asmall fraction of agents are compromised.

Original: The compromised agent either doesnot send embedded features to the FC, or sends arbitrary embedded features.
Revised: The compromised agent either doesnot send embedded features to the FC, or sends arbitrary embedded features.

Original: Toaddress this, we propose a certifiably robust COllaborative inference framework viafeature PURification (CoPur), by leveraging the block-sparse nature of adversarialperturbations on the feature vector, as well as redundancy across the embeddedfeatures (by assuming the overall features lie on an underlying lower dimensionalmanifold).
Revised: Toaddress this, we propose a certifiably robust COllaborative inference framework viafeature PURification (CoPur), by leveraging the block-sparse nature of adversarialperturbations on the feature vector, as well as redundancy across the embeddedfeatures (by assuming the overall features lie on an underlying lower dimensionalmanifold).

Original: We theoretically show that the proposed feature purification methodcan robustly recover the true feature vector, despite adversarial corruptions and/orincomplete observations.
Revised: We theoretically show that the proposed feature purification methodcan robustly recover the true feature vector, despite adversarial corruptions and/orincomplete observations.

Original: We also propose and test an untargeted distributed featureflipping attack, which is agnostic to the model, training data, label, as well as thefeatures held by other agents, and is shown to be effective in attacking state-of-theart defenses.
Revised: We also propose and test an untargeted distributed featureflipping attack, which is agnostic to the model, training data, label, as well as thefeatures held by other agents, and is shown to be effective in attacking state-of-theart defenses.

Original: Extensive experiments on ExtraSensory and NUS-WIDE datasetsshow that CoPur significantly outperforms existing defenses in terms of robustnessagainst targeted and untargeted adversarial attacks.
Revised: Extensive experiments on ExtraSensory and NUS-WIDE datasetsshow that CoPur significantly outperforms existing defenses in terms of robustnessagainst targeted and untargeted adversarial attacks.

Original:  Introduction
Revised:  Introduction

Original: Collaborative inference is an increasingly popular distributed prediction strategy that leverages diversefeatures provided by different agents (e g., sensors) for more accurate inference.
Revised: Collaborative inference is an increasingly popular distributed prediction strategy that leverages diversefeatures provided by different agents (e g., sensors) for more accurate inference.

Original: For example, incontext-aware music recommendation, the app may wish to recommend more appropriate musicbased on user’s behavioral context (e g., sleeping, running)
Revised: For example, incontext-aware music recommendation, the app may wish to recommend more appropriate musicbased on user’s behavioral context (e g., sleeping, running)

Original: (Vaizman et al., 2017).
Revised: (Vaizman et al., 2017).

Original: Here, it canleverage diverse sensors from the user’s smart phone and smart watch, e.g., phone accelerometer,gyroscope, watch accelerometer, location, audio, phone states (e.g., wifi, ringing mode), environmentsensing (e.g., light).
Revised: Here, it canleverage diverse sensors from the user’s smart phone and smart watch, e.g., phone accelerometer,gyroscope, watch accelerometer, location, audio, phone states (e.g., wifi, ringing mode), environmentsensing (e.g., light).

Original: Thus, by combining information across sensors, much more accurate predictionscan be made.
Revised: Thus, by combining information across sensors, much more accurate predictionscan be made.

Original: In this work, we study the scenario where Internet-of-Things (IoT) devices sendembedded features (instead of the raw data) to a Fusion Center (FC) for collaborative inference.
Revised: In this work, we study the scenario where Internet-of-Things (IoT) devices sendembedded features (instead of the raw data) to a Fusion Center (FC) for collaborative inference.

Original: The
Revised: The

Original: IoT devices do not communicate with each other and may hold quite different raw data (e.g., image,acoustic, Lidar data, etc).
Revised: IoT devices do not communicate with each other and may hold quite different raw data (e.g., image,acoustic, Lidar data, etc).

Original: Nevertheless, as a result of distributed inference, the system is vulnerableto attack, where a subset of sensors are corrupted by an adversary at inference time, e.g., due to thelack of effective security mechanisms, connection errors, sensor failures, among other issues – this isalso a common concern in defense applications (Abdelzaher et al., 2018).
Revised: Nevertheless, as a result of distributed inference, the system is vulnerableto attack, where a subset of sensors are corrupted by an adversary at inference time, e.g., due to thelack of effective security mechanisms, connection errors, sensor failures, among other issues – this isalso a common concern in defense applications (Abdelzaher et al., 2018).

Original: Nevertheless, the goal ofthe system is robust inference based on the possibly incomplete and corrupted embedded featuresreceived from the IoT devices.
Revised: Nevertheless, the goal ofthe system is robust inference based on the possibly incomplete and corrupted embedded featuresreceived from the IoT devices.

Original: There have been a number of works on robust parameter estimation in IoT settings, where therobustness apparently comes from redundancy among different sensors’ linear measurements of the same parameter.
Revised: There have been a number of works on robust parameter estimation in IoT settings, where therobustness apparently comes from redundancy among different sensors’ linear measurements of the same parameter.

Original: For a comprehensive overview, we refer the interested reader to (Zhang et al.,2018).
Revised: For a comprehensive overview, we refer the interested reader to (Zhang et al.,2018).

Original: However, in our considered inference setting, each IoT device may hold quite different typesof features and the redundancy is less apparent.
Revised: However, in our considered inference setting, each IoT device may hold quite different typesof features and the redundancy is less apparent.

Original: We also observe that IoT applications are oftencommunication-limited (Ko et al., 2018), thus, using lower-dimensional embeddings to communicatefrom each sensor is a common and effective approach to reduce the required communication overhead.
Revised: We also observe that IoT applications are oftencommunication-limited (Ko et al., 2018), thus, using lower-dimensional embeddings to communicatefrom each sensor is a common and effective approach to reduce the required communication overhead.

Original: The problem we study here also applies to the inference phase of some Vertical Federated Learning(VFL) frameworks (Chen et al., 2020;
Revised: The problem we study here also applies to the inference phase of some Vertical Federated Learning(VFL) frameworks (Chen et al., 2020;

Original: Liu et al., 2020), where a server similarly combines differentembedded features received from every agent to make a prediction.
Revised: Liu et al., 2020), where a server similarly combines differentembedded features received from every agent to make a prediction.

Original: One difference between oursetting and VFL is: the agents in VFL are other untrusted parties, if an agent in VFL is malicious,it will likely attack during both training and inference; while in our setting, the agents (e.g., IoTsensors) are all owned by FC, and they can be well protected during training, and inference is morevulnerable.
Revised: One difference between oursetting and VFL is: the agents in VFL are other untrusted parties, if an agent in VFL is malicious,it will likely attack during both training and inference; while in our setting, the agents (e.g., IoTsensors) are all owned by FC, and they can be well protected during training, and inference is morevulnerable.

Original: So far, there are no certifiable defenses for VFL against adversarial attacks during theinference phase.
Revised: So far, there are no certifiable defenses for VFL against adversarial attacks during theinference phase.

Original: The well-known robust aggregation method (Yin et al., 2018; Guerraoui et al., 2018;
Revised: The well-known robust aggregation method (Yin et al., 2018; Guerraoui et al., 2018;

Original: Blanchard et al., 2017; Fu et al., 2019; Pillutla et al., 2019; Fung et al., 2020; Chen et al., 2017; Xieet al., 2019) used for Horizontal Federated Learning (HFL) is not applicable.
Revised: Blanchard et al., 2017; Fu et al., 2019; Pillutla et al., 2019; Fung et al., 2020; Chen et al., 2017; Xieet al., 2019) used for Horizontal Federated Learning (HFL) is not applicable.

Original: Other related works will be discussed in Section 4.
Revised: Other related works will be discussed in Section 4.

Original: We use the term ‘device’ and ’agent’ interchangeably in this paper.
Revised: We use the term ‘device’ and ’agent’ interchangeably in this paper.

Original: The main difficulty of defending against adversarial attacks in our collaborativeinference setting (as well as in VFL) is that there is no apparent redundancy across the agents.
Revised: The main difficulty of defending against adversarial attacks in our collaborativeinference setting (as well as in VFL) is that there is no apparent redundancy across the agents.

Original: Though,intuitively, there is some mutual information among the features held by each agent.
Revised: Though,intuitively, there is some mutual information among the features held by each agent.

Original: Even worse, theraw data held by the agents are not accessible by the FC/server to inspect, the attacker can perturb thecompromised agents’ embedded features with very large magnitude before sending to the FC/server.
Revised: Even worse, theraw data held by the agents are not accessible by the FC/server to inspect, the attacker can perturb thecompromised agents’ embedded features with very large magnitude before sending to the FC/server.

Original: In this paper, we propose to improve robustness based on the underlying redundancy among thefeatures held by the IoT devices.
Revised: In this paper, we propose to improve robustness based on the underlying redundancy among thefeatures held by the IoT devices.

Original: Specifically, we assume the combined features lie on an underlyinglower dimensional manifold.
Revised: Specifically, we assume the combined features lie on an underlyinglower dimensional manifold.

Original: We propose a robust COllaborative inference framework via feature
Revised: We propose a robust COllaborative inference framework via feature

Original: PURification (CoPur), which is able to defend against many types of attacks during inference (seethe attack taxonomy in Section 2.2).
Revised: PURification (CoPur), which is able to defend against many types of attacks during inference (seethe attack taxonomy in Section 2.2).

Original: During the inference, the FC receives the corrupted and/orincomplete embedded features from agents (Figure 1a).
Revised: During the inference, the FC receives the corrupted and/orincomplete embedded features from agents (Figure 1a).

Original: Then CoPur purifies the embedded featuresbased on the learned feature subspace (Figure 1b).
Revised: Then CoPur purifies the embedded featuresbased on the learned feature subspace (Figure 1b).

Original: Finally, it feeds the purified features to FC’strained global model for prediction.
Revised: Finally, it feeds the purified features to FC’strained global model for prediction.

Original: The detailed robust inference procedures will be described in
Revised: The detailed robust inference procedures will be described in

Original: Sections 2.3.
Revised: Sections 2.3.

Original: We aim to answer the following questions: Can we make robust predictions based oncorrupted and/or incomplete features?
Revised: We aim to answer the following questions: Can we make robust predictions based oncorrupted and/or incomplete features?

Original: If yes, how many compromised devices can we tolerate?
Revised: If yes, how many compromised devices can we tolerate?

Original: Technical Contributions : Our main contributions include the following:
Revised: Technical Contributions : Our main contributions include the following:

Original: • We propose a certifiably robust collaborative inference procedure to defend against a variety oftargeted or untargeted attacks (e.g., distributed adversarial attacks, missing-feature attack, and theircombinations) during collaborative inference.•
Revised: • We propose a certifiably robust collaborative inference procedure to defend against a variety oftargeted or untargeted attacks (e.g., distributed adversarial attacks, missing-feature attack, and theircombinations) during collaborative inference.•

Original: We propose a novel non-linear robust decomposition method that decomposes the potentiallycorrupted and incomplete feature vector into two parts: one lies (exactly or approximately) on theunderlying feature manifold; and the other with a block-sparse structure.
Revised: We propose a novel non-linear robust decomposition method that decomposes the potentiallycorrupted and incomplete feature vector into two parts: one lies (exactly or approximately) on theunderlying feature manifold; and the other with a block-sparse structure.

Original: Theoretically, we provethat the proposed method can recover the true feature vector exactly (or approximately ) despitecorruptions and incomplete observations.
Revised: Theoretically, we provethat the proposed method can recover the true feature vector exactly (or approximately ) despitecorruptions and incomplete observations.

Original: This may be of independent interest.
Revised: This may be of independent interest.

Original: • We propose and test a distributed feature-flipping attack, which is agnostic to the model, trainingdata, label, as well as the features held by other agents.
Revised: • We propose and test a distributed feature-flipping attack, which is agnostic to the model, trainingdata, label, as well as the features held by other agents.

Original: Empirical studies show that this new attackis very effective in attacking existing state-of-the-art defenses.•
Revised: Empirical studies show that this new attackis very effective in attacking existing state-of-the-art defenses.•

Original: We conduct extensive experiments on ExtraSensory and NUS-WIDE datasets
Revised: We conduct extensive experiments on ExtraSensory and NUS-WIDE datasets

Original: and show that CoPuris significantly more robust than baselines against different types of attacks.
Revised: and show that CoPuris significantly more robust than baselines against different types of attacks.

Original:  Robust collaborative inference via feature purification (CoPur)
Revised:  Robust collaborative inference via feature purification (CoPur)

Original: We describe the system setting, the threat models and the proposed robust inference procedures.
Revised: We describe the system setting, the threat models and the proposed robust inference procedures.

Original: 2.1 Collaborative inference; System setting
Revised: 2.1 Collaborative inference; System setting

Original: We first describe the basic framework of collaborative inference, which is also similar to the inferencephase of some VFL frameworks (Chen et al., 2020; Liu et al., 2020).
Revised: We first describe the basic framework of collaborative inference, which is also similar to the inferencephase of some VFL frameworks (Chen et al., 2020; Liu et al., 2020).

Original: There are M agents, where eachagent i holds partial raw data x testi of the overall testing sample x test =
Revised: There are M agents, where eachagent i holds partial raw data x testi of the overall testing sample x test =

Original: [ x test; ... ; x testM ] .
Revised: [ x test; ... ; x testM ] .

Original: Also, eachagent i has its own local feature extractor f i parameterized by θ i that maps its raw data vector x testito the embedded feature vector h testi ≜
Revised: Also, eachagent i has its own local feature extractor f i parameterized by θ i that maps its raw data vector x testito the embedded feature vector h testi ≜

Original: f i ( x testi ; θ i ) .
Revised: f i ( x testi ; θ i ) .

Original: The dimension of h testi is usually smaller thanx testi .
Revised: The dimension of h testi is usually smaller thanx testi .

Original: The FC only receives the embedded features h testi , i = 1 , ..., M from the agents (not the rawdata x testi ), and concatenates them into a long column vector h test ≜ [ h test; h test; ... ; h testM ] .
Revised: The FC only receives the embedded features h testi , i = 1 , ..., M from the agents (not the rawdata x testi ), and concatenates them into a long column vector h test ≜ [ h test; h test; ... ; h testM ] .

Original: Thenthe FC uses its pre-trained global model g θ 0 to make the prediction.
Revised: Thenthe FC uses its pre-trained global model f θ 0 to make the prediction.

Original: While we do not focus on training, we will require an AutoEncoder, trained on uncorrupted data (e.g.,during training) that can approximately capture the manifold of the uncorrupted embedded features,i.e. , (cid:80) Mi =1 ∥
Revised: While we do not focus on training, we will require an AutoEncoder, trained on uncorrupted data (e.g.,during training) that can approximately capture the manifold of the uncorrupted embedded features,i.e. , (cid:80) Mi =1 ∥

Original: [ D ϕ ( E ψ ( l ∗ )) − l ∗ ] i ∥ 2 ≤ δ for the true embedded features l ∗ of the testing instance,where D ϕ is the decoder parameterized by ϕ , and E ψ is the encoder parameterized by ψ .
Revised: [ D ϕ ( E ψ ( l ∗ )) − l ∗ ] i ∥ 2 ≤ δ for the true embedded features l ∗ of the testing instance,where D ϕ is the decoder parameterized by ϕ , and E ψ is the encoder parameterized by ψ .

Original: Further, weassume that the global model for inference g θ 0 is based on the output of the AutoEncoder and thetraining labels, i.e. , {D ϕ ( E ψ ( h ( j ) )) , y ( j ) } nj =1 .
Revised: Further, weassume that the global model for inference f θ 0 is based on the output of the AutoEncoder and thetraining labels, i.e. , {D ϕ ( E ψ ( h ( j ) )) , y ( j ) } nj =1 .

Original: Detailed examples of how to train such a model andthe AutoEncoder can be found in Section D of the supplemental material.
Revised: Detailed examples of how to train such a model andthe AutoEncoder can be found in Section D of the supplemental material.

Original: 2.2 Threat model
Revised: 2.2 Threat model

Original: There are M agents which hold different parts of the feature of the same set of n training instances.
Revised: There are M agents which hold different parts of the feature of the same set of n training instances.

Original: We assume the training process is well-protected (attack-free).
Revised: We assume the training process is well-protected (attack-free).

Original: However, during the inference, theattacker can attack αM agents.
Revised: However, during the inference, theattacker can attack αM agents.

Original: The compromised agent either is not be able to send features to the
Revised: The compromised agent either is not be able to send features to the

Original: FC, or sends arbitrary embedded features to FC.
Revised: FC, or sends arbitrary embedded features to FC.

Original: We use h benign to denote the overall embedded features provided by the (1 − α )
Revised: We use h benign to denote the overall embedded features provided by the (1 − α )

Original: M benign agents(these agents are indexed by Ω benign and | Ω benign | = (1 − α ) M ), and we use h adv to denotethe overall embedded features sent by compromised agents (indexed by Ω adv ).
Revised: M benign agents(these agents are indexed by Ω benign and | Ω benign | = (1 − α ) M ), and we use h adv to denotethe overall embedded features sent by compromised agents (indexed by Ω adv ).

Original: The set Ω =Ω benign ∪
Revised: The set Ω =Ω benign ∪

Original: Ω adv is the index set of agents that provide features, and its complement, i.e. , Ω c , denotesthe compromised agents which do not provide features.
Revised: Ω adv is the index set of agents that provide features, and its complement, i.e. , Ω c , denotesthe compromised agents which do not provide features.

Original: We categorize the inference phase attacksinto the following three types:
Revised: We categorize the inference phase attacksinto the following three types:

Original: Threat model A (distributed adversarial attack): All the compromised agents jointly send adversarial embedded features h adv , with the goal to mislead the prediction to the target label
Revised: Threat model A (distributed adversarial attack): All the compromised agents jointly send adversarial embedded features h adv , with the goal to mislead the prediction to the target label

Original: y A :
Revised: y A :

Original: [Equation]
Revised: [Equation]

Original: Threat model B (missing-feature attack): All the compromised agents do not send any feature tothe FC.
Revised: Threat model B (missing-feature attack): All the compromised agents do not send any feature tothe FC.

Original: Such missing-feature attack may due to the damaged sensors or disconnection, and can stillaffect FC’s prediction.
Revised: Such missing-feature attack may due to the damaged sensors or disconnection, and can stillaffect FC’s prediction.

Original: The presence of missing-feature attack can be viewed as the FC observingonly partial blocks (indexed by Ω ) of the overall embedded features h , i.e. , h Ω .
Revised: The presence of missing-feature attack can be viewed as the FC observingonly partial blocks (indexed by Ω ) of the overall embedded features h , i.e. , h Ω .

Original: Ω c denotes the indexset of agents that perform missing-feature attacks.
Revised: Ω c denotes the indexset of agents that perform missing-feature attacks.

Original: The attacker can compromise up to αM agents toperform such attack:
Revised: The attacker can compromise up to αM agents toperform such attack:

Original: [Equation]
Revised: [Equation]

Original: Threat model C (combined attack):
Revised: Threat model C (combined attack):

Original: The attacker can also perform a combined adversarial andmissing feature attack by providing h adv , indexed by Ω adv , and missing features Ω c :
Revised: The attacker can also perform a combined adversarial andmissing feature attack by providing h adv , indexed by Ω adv , and missing features Ω c :

Original: [Equation]
Revised: [Equation]

Original: Beyond these targeted attacks, the attacker can also perform untargeted attacks by maximizing thedistance between the prediction and the ground truth label.
Revised: Beyond these targeted attacks, the attacker can also perform untargeted attacks by maximizing thedistance between the prediction and the ground truth label.

Original: 2.3 Inference procedures of CoPur
Revised: 2.3 Inference procedures of CoPur

Original: We propose an all-in-one solution to defend against various inference phase attacks defined in
Revised: We propose an all-in-one solution to defend against various inference phase attacks defined in

Original: Section 2.2.
Revised: Section 2.2.

Original: Let Ω be the index set of the observed blocks.
Revised: Let Ω be the index set of the observed blocks.

Original: The observed h Ω can be decomposed ash Ω = l Ω + e Ω where l is the underlying uncorrupted features, and e Ω models the corruptions onthe observed set Ω .
Revised: The observed h Ω can be decomposed ash Ω = l Ω + e Ω where l is the underlying uncorrupted features, and e Ω models the corruptions onthe observed set Ω .

Original: Finding such a decomposition seems hard, even if the learned AutoEncoder can capture the underlying feature subspace, i.e. , l = D ϕ
Revised: Finding such a decomposition seems hard, even if the learned AutoEncoder can capture the underlying feature subspace, i.e. , l = D ϕ

Original: ( E ψ ( l )) .
Revised: ( E ψ ( l )) .

Original: Fortunately, the perturbation e has ablock-wise structure (each block corresponds to an agent), and the fraction of the perturbed blocks issmall.
Revised: Fortunately, the perturbation e has ablock-wise structure (each block corresponds to an agent), and the fraction of the perturbed blocks issmall.

Original: Therefore, e Ω has a block-sparse structure.
Revised: Therefore, e Ω has a block-sparse structure.

Original: We propose to solve the following:
Revised: We propose to solve the following:

Original: [Equation]
Revised: [Equation]

Original: where the indicator function 1 {·} counts whether the block [ h − l ] i is nonzero.
Revised: where the indicator function 1 {·} counts whether the block [ h − l ] i is nonzero.

Original: This block-sparsityminimization shares the same spirit as the ℓ 0 minimization in robust linear regression (Candes & Tao,2005; Mitra et al., 2013).
Revised: This block-sparsityminimization shares the same spirit as the ℓ 0 minimization in robust linear regression (Candes & Tao,2005; Mitra et al., 2013).

Original: However, it is NP-hard to solve, so we relax it to the ℓ 2 , 1 -norm:
Revised: However, it is NP-hard to solve, so we relax it to the ℓ 2 , 1 -norm:

Original: [Equation]
Revised: [Equation]

Original: Our theoretical analysis in Theorems 1 & 2 show that under certain conditions, solving the aboverelaxed objective is able to recover the underlying true features exactly.
Revised: Our theoretical analysis in Theorems 1 & 2 show that under certain conditions, solving the aboverelaxed objective is able to recover the underlying true features exactly.

Original: But what if the learned AutoEncoder can only approximately reconstruct the underlying features, say 137 (cid:80)
Revised: But what if the learned AutoEncoder can only approximately reconstruct the underlying features, say 137 (cid:80)

Original: [Equation]
Revised: [Equation]

Original: =1 ∥
Revised: =1 ∥

Original: [ D ϕ
Revised: [ D ϕ

Original: [Equation]
Revised: [Equation]

Original: E ψ ( l )
Revised: E ψ ( l )

Original: [Equation]
Revised: [Equation]

Original: − l ] i ∥ 2 ≤ δ ?
Revised: − l ] i ∥ 2 ≤ δ ?

Original: In this case, we further relax the constraints in Eq.
Revised: In this case, we further relax the constraints in Eq.

Original: 5 and solve:
Revised: 5 and solve:

Original: [Equation]
Revised: [Equation]

Original: Our theoretical analysis in Theorems 3 & 4 show that under certain conditions, solving Eq.
Revised: Our theoretical analysis in Theorems 3 & 4 show that under certain conditions, solving Eq.

Original: 6 is ableto approximately recover the underlying true features, despite the corruptions and/or incompleteobservations.
Revised: 6 is ableto approximately recover the underlying true features, despite the corruptions and/or incompleteobservations.

Original: After obtaining ˆ l , we feed D ϕ ( E ψ (ˆ l )) to FC’s trained global model for the prediction.
Revised: After obtaining ˆ l , we feed D ϕ ( E ψ (ˆ l )) to FC’s trained global model for the prediction.

Original:  Theoretical analysis of CoPur
Revised:  Theoretical analysis of CoPur

Original: We first analyze the ‘noiseless case’ where the learned AutoEncoder satisfies D ϕ
Revised: We first analyze the ‘noiseless case’ where the learned AutoEncoder satisfies D ϕ

Original: ( E ψ ( l ∗ )) =
Revised: ( E ψ ( l ∗ )) =

Original: l ∗ forthe underlying uncorrupted feature vector l ∗ .
Revised: l ∗ forthe underlying uncorrupted feature vector l ∗ .

Original: Next, we analyze the more challenging ‘noisy case’where the AutoEncoder can only approximately reconstruct l ∗ , i.e. , (cid:80) Mi
Revised: Next, we analyze the more challenging ‘noisy case’where the AutoEncoder can only approximately reconstruct l ∗ , i.e. , (cid:80) Mi

Original: =1 ∥ [ D ϕ ( E ψ ( l ∗ )) − l ∗ ] i ∥ 2 ≤ δ .
Revised: =1 ∥ [ D ϕ ( E ψ ( l ∗ )) − l ∗ ] i ∥ 2 ≤ δ .

Original: Proofs and additional discussions can be found in Section A of the supplemental material.
Revised: Proofs and additional discussions can be found in Section A of the supplemental material.

Original: 3.1 Noiseless Case
Revised: 3.1 Noiseless Case

Original: To simplify the exposition, we first consider threat model B, i.e. , the missing-feature attack.
Revised: To simplify the exposition, we first consider threat model B, i.e. , the missing-feature attack.

Original: Theorem 1.
Revised: Theorem 1.

Original: (Exact feature recovery under threat model B) Assume the trained AutoEncodersatisfies D ϕ ( E ψ ( l ∗ ))
Revised: (Exact feature recovery under threat model B) Assume the trained AutoEncodersatisfies D ϕ ( E ψ ( l ∗ ))

Original: =
Revised: =

Original: l ∗ for the underlying uncorrupted feature vector l ∗ .
Revised: l ∗ for the underlying uncorrupted feature vector l ∗ .

Original: For ∀ r ′ , r ′′ in the rangeof the decoder D ϕ ( · ) where r ′ ̸ = r ′′ , if ( r ′ − r ′′ )
Revised: For ∀ r ′ , r ′′ in the rangeof the decoder D ϕ ( · ) where r ′ ̸ = r ′′ , if ( r ′ − r ′′ )

Original: Ω ̸ = 0 , then given h Ω =
Revised: Ω ̸ = 0 , then given h Ω =

Original: l ∗ Ω , l ∗ is the uniquesolution of Eq.
Revised: l ∗ Ω , l ∗ is the uniquesolution of Eq.

Original: 5 and Eq. 4.
Revised: 5 and Eq. 4.

Original: The proof can be found in Section A of the supplemental material, where we show that any feasiblepoint that is different from l ∗ would have larger objective value.
Revised: The proof can be found in Section A of the supplemental material, where we show that any feasiblepoint that is different from l ∗ would have larger objective value.

Original: The condition ( r ′ − r ′′ ) Ω ̸ = 0 required by Theorem 1 is intuitive and necessary.
Revised: The condition ( r ′ − r ′′ ) Ω ̸ = 0 required by Theorem 1 is intuitive and necessary.

Original: Suppose thereare r ′ ̸ = r ′′ but r ′ Ω = r ′′ Ω , then observing
Revised: Suppose thereare r ′ ̸ = r ′′ but r ′ Ω = r ′′ Ω , then observing

Original: r
Revised: r

Original: Ω is not able to tell whether it’s from r ′ or r ′′ .
Revised: Ω is not able to tell whether it’s from r ′ or r ′′ .

Original: Ingeneral, when the observed set Ω gets larger, the required condition has better chance to be satisfied.
Revised: Ingeneral, when the observed set Ω gets larger, the required condition has better chance to be satisfied.

Original: If we have complete observations, i.e. , Ω = { 1 , 2 , ..., M } , the required condition ( r ′ − r ′′ ) Ω ̸
Revised: If we have complete observations, i.e. , Ω = { 1 , 2 , ..., M } , the required condition ( r ′ − r ′′ ) Ω ̸

Original: = 0automatically holds .
Revised: = 0automatically holds .

Original: Now we consider the more challenging case where we not only have missing features, but also havecorruptions on the observed features.
Revised: Now we consider the more challenging case where we not only have missing features, but also havecorruptions on the observed features.

Original: Theorem 2.
Revised: Theorem 2.

Original: (Exact feature recovery under threat model C) Assume the trained AutoEncodersatisfies D ϕ
Revised: (Exact feature recovery under threat model C) Assume the trained AutoEncodersatisfies D ϕ

Original: ( E ψ ( l ∗ )) =
Revised: ( E ψ ( l ∗ )) =

Original: l ∗ for the underlying uncorrupted feature vector l ∗ .
Revised: l ∗ for the underlying uncorrupted feature vector l ∗ .

Original: For ∀ r ′ , r ′′ in the rangeof the decoder D ϕ ( · ) where r ′ ̸ = r ′′ , define v = r ′ − r ′′ , if for any partition { S Ω , ¯ S Ω } of Ω with| ¯ S Ω | = q < | Ω | / 2 , it holds that (cid:80) i ∈ S Ω
Revised: For ∀ r ′ , r ′′ in the rangeof the decoder D ϕ ( · ) where r ′ ̸ = r ′′ , define v = r ′ − r ′′ , if for any partition { S Ω , ¯ S Ω } of Ω with| ¯ S Ω | = q < | Ω | / 2 , it holds that (cid:80) i ∈ S Ω

Original: ∥ v i ∥ 2 > (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 , then for any h Ω =
Revised: ∥ v i ∥ 2 > (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 , then for any h Ω =

Original: l ∗ Ω + e ∗ Ω with 166 (cid:80) i ∈
Revised: l ∗ Ω + e ∗ Ω with 166 (cid:80) i ∈

Original: Ω 1 { e ∗ i ̸ = 0 } ≤ q , l ∗ is the unique solution of Eq.
Revised: Ω 1 { e ∗ i ̸ = 0 } ≤ q , l ∗ is the unique solution of Eq.

Original: 5 and Eq. 4.
Revised: 5 and Eq. 4.

Original: The proof can be found in Section A of the supplemental material.
Revised: The proof can be found in Section A of the supplemental material.

Original: We first show that any feasiblepoint of Eq.
Revised: We first show that any feasiblepoint of Eq.

Original: 5 that is different from l ∗ would have larger objective value.
Revised: 5 that is different from l ∗ would have larger objective value.

Original: Then we use contradictionto show that there does not exist any global optimal solution of Eq.
Revised: Then we use contradictionto show that there does not exist any global optimal solution of Eq.

Original: 4 that is different from l ∗ .
Revised: 4 that is different from l ∗ .

Original: Remark 1.
Revised: Remark 1.

Original: Theorem 2 not only requires v Ω ̸ = 0 like Theorem 1, but also requires (cid:80)
Revised: Theorem 2 not only requires v Ω ̸ = 0 like Theorem 1, but also requires (cid:80)

Original: i ∈
Revised: i ∈

Original: S Ω
Revised: S Ω

Original: ∥ v i ∥ 2 > 171 (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 .
Revised: ∥ v i ∥ 2 > 171 (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 .

Original: This is due to the presence of corruptions in the observed blocks (indexed by ¯ S Ω ).
Revised: This is due to the presence of corruptions in the observed blocks (indexed by ¯ S Ω ).

Original: When there is no corruption, i.e. , q = 0 , the corresponding set ¯ S Ω is empty and the required conditionreduces to (cid:80) i ∈
Revised: When there is no corruption, i.e. , q = 0 , the corresponding set ¯ S Ω is empty and the required conditionreduces to (cid:80) i ∈

Original: Ω
Revised: Ω

Original: ∥ v i ∥ 2 > 0 , i.e. , v Ω ̸ = 0 .
Revised: ∥ v i ∥ 2 > 0 , i.e. , v Ω ̸ = 0 .

Original: Second, Theorem 2 is a universal certification, whereany subset ¯ S Ω of the observations can be malicious, as long as | ¯ S Ω | = q .
Revised: Second, Theorem 2 is a universal certification, whereany subset ¯ S Ω of the observations can be malicious, as long as | ¯ S Ω | = q .

Original: In practice, once theattacker chooses q agents to perform adversarial attacks, the set ¯ S Ω gets fixed and corresponds toe ∗ i ̸ = 0 .
Revised: In practice, once theattacker chooses q agents to perform adversarial attacks, the set ¯ S Ω gets fixed and corresponds toe ∗ i ̸ = 0 .

Original: Then, we only require (cid:80) i ∈
Revised: Then, we only require (cid:80) i ∈

Original: S Ω
Revised: S Ω

Original: ∥ v i ∥ 2 > (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 to hold for this particular fixed set ¯ S Ω ,to guarantee l ∗ is the unique solution of Eq.
Revised: ∥ v i ∥ 2 > (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 to hold for this particular fixed set ¯ S Ω ,to guarantee l ∗ is the unique solution of Eq.

Original: This can be easily seen from the proof of Theorem 2.
Revised: This can be easily seen from the proof of Theorem 2.

Original: Third, the condition (cid:80) i ∈ S Ω
Revised: Third, the condition (cid:80) i ∈ S Ω

Original: ∥ v i ∥ 2 > (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 prefers v Ω to be not ‘spiky’.
Revised: ∥ v i ∥ 2 > (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 prefers v Ω to be not ‘spiky’.

Original: Suppose r ′ and r ′′are the same on all the observed blocks (indexed by Ω ) except one, which means only one blockof v Ω is non-zero ( i.e. , v Ω is spiky).
Revised: Suppose r ′ and r ′′are the same on all the observed blocks (indexed by Ω ) except one, which means only one blockof v Ω is non-zero ( i.e. , v Ω is spiky).

Original: If that non-zero block is in the set ¯ S Ω , the required conditionwill not be met.
Revised: If that non-zero block is in the set ¯ S Ω , the required conditionwill not be met.

Original: Intuitively, if there are two instances differing in only one block of the observedfeatures, and the attacker corrupts that block, the FC can not recover the original feature.
Revised: Intuitively, if there are two instances differing in only one block of the observedfeatures, and the attacker corrupts that block, the FC can not recover the original feature.

Original: In general,we prefer v Ω to be not ‘spiky’, thus the condition can hold when the number of corrupted blocks | ¯ S Ω |is sufficiently small.
Revised: In general,we prefer v Ω to be not ‘spiky’, thus the condition can hold when the number of corrupted blocks | ¯ S Ω |is sufficiently small.

Original: Such condition can be considered as the non-linear and missing-block extensionsof the Range Space Property in robust linear regression literature (Flores, 2015; Liu et al., 2018),which we will discuss next.
Revised: Such condition can be considered as the non-linear and missing-block extensionsof the Range Space Property in robust linear regression literature (Flores, 2015; Liu et al., 2018),which we will discuss next.

Original: Finally, the fraction ( q/M ) of corrupted blocks that CoPur can toleratenot only depends on Ω , but also depends on the underlying feature subspace.
Revised: Finally, the fraction ( q/M ) of corrupted blocks that CoPur can toleratenot only depends on Ω , but also depends on the underlying feature subspace.

Original: It can tolerate up to50% ×| Ω | corrupted blocks if there is enough redundancy among the agents, e.g., if every agent holdsexactly the same embedded features, then every block of v = r ′ − r ′′ is same, and q approaches50% ×| Ω | .
Revised: It can tolerate up to50% ×| Ω | corrupted blocks if there is enough redundancy among the agents, e.g., if every agent holdsexactly the same embedded features, then every block of v = r ′ − r ′′ is same, and q approaches50% ×| Ω | .

Original: Remark 2.
Revised: Remark 2.

Original: To draw the connections to the Range Space Property in robust linear regression, let’sconsider a typical case where there is no non-linear activation function in the last layer of the
Revised: To draw the connections to the Range Space Property in robust linear regression, let’sconsider a typical case where there is no non-linear activation function in the last layer of the

Original: AutoEncoder (other layers can have non-linear activation functions).
Revised: AutoEncoder (other layers can have non-linear activation functions).

Original: Let W end be the weight matrixof the last layer of the AutoEncoder.
Revised: Let W end be the weight matrixof the last layer of the AutoEncoder.

Original: Note that the difference between any two vectors from the rangeof the decoder D ϕ ( · ) is contained in the Range ( W end ) , i.e. , v ∈ Range ( W end ) \ 0 .
Revised: Note that the difference between any two vectors from the rangeof the decoder D ϕ ( · ) is contained in the Range ( W end ) , i.e. , v ∈ Range ( W end ) \ 0 .

Original: Similar to the socalled ‘leverage constant’ in robust linear regression (Flores, 2015; Liu et al., 2018), we can define theblock-wise ‘incomplete’ leverage constant
Revised: Similar to the socalled ‘leverage constant’ in robust linear regression (Flores, 2015; Liu et al., 2018), we can define theblock-wise ‘incomplete’ leverage constant

Original: [Equation]
Revised: [Equation]

Original: which is between 0 and 1, and is monotonic increasing when the number of corrupted blocks qdecreases.
Revised: which is between 0 and 1, and is monotonic increasing when the number of corrupted blocks qdecreases.

Original: A sufficient condition for (cid:80) i ∈ S Ω
Revised: A sufficient condition for (cid:80) i ∈ S Ω

Original: ∥ v i ∥ 2 − (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 > 0 to hold is c Ω q ( W end ) > 0 .
Revised: ∥ v i ∥ 2 − (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 > 0 to hold is c Ω q ( W end ) > 0 .

Original: Lastly, it is very useful to note that Sharon et al.
Revised: Lastly, it is very useful to note that Sharon et al.

Original: (2009) designed an algorithm to calculate theleverage constant, which is possible to be extended to our block-wise incomplete leverage constanthere.
Revised: (2009) designed an algorithm to calculate theleverage constant, which is possible to be extended to our block-wise incomplete leverage constanthere.

Original: Consider the toy example again where every agent holds exactly the same embedded features,and q = 0 .
Revised: Consider the toy example again where every agent holds exactly the same embedded features,and q = 0 .

Original: 1 × | Ω | , then c Ω q ( W end ) = 0 .
Revised: 1 × | Ω | , then c Ω q ( W end ) = 0 .

Original: Remark 3.
Revised: Remark 3.

Original: It is interesting to note that in the recent Generative model based compressive sensingwork (Bora et al., 2017), they also impose conditions on the difference between any two vectors in therange of the decoder, though without missing blocks.
Revised: It is interesting to note that in the recent Generative model based compressive sensingwork (Bora et al., 2017), they also impose conditions on the difference between any two vectors in therange of the decoder, though without missing blocks.

Original: But the conditions therein are quite differentfrom ours due to the different purposes.
Revised: But the conditions therein are quite differentfrom ours due to the different purposes.

Original: 3.2 Noisy case
Revised: 3.2 Noisy case

Original: Now we analyze the ‘noisy case’ where the learned AutoEncoder can only approximately reconstructthe underlying uncorrupted feature vector l ∗ , i.e. , (cid:80) Mi
Revised: Now we analyze the ‘noisy case’ where the learned AutoEncoder can only approximately reconstructthe underlying uncorrupted feature vector l ∗ , i.e. , (cid:80) Mi

Original: =1 ∥ [ D ϕ ( E ψ ( l ∗ )) − l ∗ ] i ∥ 2 ≤ δ .
Revised: =1 ∥ [ D ϕ ( E ψ ( l ∗ )) − l ∗ ] i ∥ 2 ≤ δ .

Original: Again, to helpthe reader understand step-by-step, our analyses start from the threat model B.
Revised: Again, to helpthe reader understand step-by-step, our analyses start from the threat model B.

Original: Theorem 3.
Revised: Theorem 3.

Original: (Stable feature recovery under threat model B) Assume the trained AutoEncodersatisfies (cid:80)
Revised: (Stable feature recovery under threat model B) Assume the trained AutoEncodersatisfies (cid:80)

Original: Mi
Revised: Mi

Original: =1 ∥ [ D ϕ ( E ψ ( l ∗ )) − l ∗ ] i ∥ 2 ≤ δ for the underlying uncorrupted feature vector l ∗ .
Revised: =1 ∥ [ D ϕ ( E ψ ( l ∗ )) − l ∗ ] i ∥ 2 ≤ δ for the underlying uncorrupted feature vector l ∗ .

Original: Let ˆ lbe the solution of Eq. 6.
Revised: Let ˆ lbe the solution of Eq. 6.

Original: Given the incomplete observations h Ω =
Revised: Given the incomplete observations h Ω =

Original: l ∗ Ω , where Ω is the index set ofobserved agent blocks.
Revised: l ∗ Ω , where Ω is the index set ofobserved agent blocks.

Original: For ∀ r ′ , r ′′ in the range of the decoder D ϕ ( · ) , define v = r ′ − r ′′ , if it holdsthat (cid:80)
Revised: For ∀ r ′ , r ′′ in the range of the decoder D ϕ ( · ) , define v = r ′ − r ′′ , if it holdsthat (cid:80)

Original: i ∈
Revised: i ∈

Original: Ω
Revised: Ω

Original: ∥ v i ∥ 2 > 2 δ for ∀∥ v ∥ 2 > ∆ , then ∥D ϕ ( E ψ (ˆ l )) − D ϕ
Revised: ∥ v i ∥ 2 > 2 δ for ∀∥ v ∥ 2 > ∆ , then ∥D ϕ ( E ψ (ˆ l )) − D ϕ

Original: ( E ψ ( l ∗ ))
Revised: ( E ψ ( l ∗ ))

Original: ∥ 2 ≤ ∆ .
Revised: ∥ 2 ≤ ∆ .

Original: The proof can be found in Section A of the supplemental material.
Revised: The proof can be found in Section A of the supplemental material.

Original: Remark 4.
Revised: Remark 4.

Original: First, note that (cid:80) Mi
Revised: First, note that (cid:80) Mi

Original: =1 ∥ v i ∥ 2 ≥ (cid:113)(cid:80)
Revised: =1 ∥ v i ∥ 2 ≥ (cid:113)(cid:80)

Original: Mi
Revised: Mi

Original: =1 ∥ v i ∥ 22 = ∥ v ∥ 2 .
Revised: =1 ∥ v i ∥ 22 = ∥ v ∥ 2 .

Original: When ∥ v ∥ 2 > ∆ , we have(cid:80) Mi =1 ∥ v i ∥ 2 > ∆ .
Revised: When ∥ v ∥ 2 > ∆ , we have(cid:80) Mi =1 ∥ v i ∥ 2 > ∆ .

Original: Again, we prefer v to be not ‘spiky’, thus the condition (cid:80) i ∈
Revised: Again, we prefer v to be not ‘spiky’, thus the condition (cid:80) i ∈

Original: Ω ∥ v i ∥ 2 > 2 δ can besatisfied as long as | Ω | is large enough (assume ∆ > 2 δ ).
Revised: Ω ∥ v i ∥ 2 > 2 δ can besatisfied as long as | Ω | is large enough (assume ∆ > 2 δ ).

Original: Now we consider the most challenging case where we not only have missing features, but also havecorruptions in the observed features.
Revised: Now we consider the most challenging case where we not only have missing features, but also havecorruptions in the observed features.

Original: Theorem 4.
Revised: Theorem 4.

Original: (Stable feature recovery under threat model C) Assume the trained AutoEncodersatisfies (cid:80)
Revised: (Stable feature recovery under threat model C) Assume the trained AutoEncodersatisfies (cid:80)

Original: Mi
Revised: Mi

Original: =1 ∥ [ D ϕ ( E ψ ( l ∗ )) − l ∗ ] i ∥ 2 ≤ δ for the underlying uncorrupted feature vector l ∗ .
Revised: =1 ∥ [ D ϕ ( E ψ ( l ∗ )) − l ∗ ] i ∥ 2 ≤ δ for the underlying uncorrupted feature vector l ∗ .

Original: Letˆ l be the solution of Eq. 6.
Revised: Letˆ l be the solution of Eq. 6.

Original: Given the incomplete observations h Ω =
Revised: Given the incomplete observations h Ω =

Original: l ∗ Ω + e ∗ Ω , where Ω is theindex set of observed agent blocks, define S Ω as the subset of Ω corresponds to e ∗ i = 0 ,
Revised: l ∗ Ω + e ∗ Ω , where Ω is theindex set of observed agent blocks, define S Ω as the subset of Ω corresponds to e ∗ i = 0 ,

Original: and¯ S Ω = Ω \ S Ω .
Revised: and¯ S Ω = Ω \ S Ω .

Original: For ∀ r ′ , r ′′ in the range of the decoder D ϕ ( · ) , define v = r ′ − r ′′ , if it holds that(cid:80)
Revised: For ∀ r ′ , r ′′ in the range of the decoder D ϕ ( · ) , define v = r ′ − r ′′ , if it holds that(cid:80)

Original: i ∈ S Ω
Revised: i ∈ S Ω

Original: ∥ v i ∥ 2 − (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 > 2 δ for ∀∥ v ∥ 2 > ∆ , then ∥D ϕ
Revised: ∥ v i ∥ 2 − (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 > 2 δ for ∀∥ v ∥ 2 > ∆ , then ∥D ϕ

Original: ( E ψ (ˆ l ))
Revised: ( E ψ (ˆ l ))

Original: − D ϕ
Revised: − D ϕ

Original: ( E ψ ( l ∗ ))
Revised: ( E ψ ( l ∗ ))

Original: ∥ 2 ≤ ∆ .
Revised: ∥ 2 ≤ ∆ .

Original: The proof can be found in Section A of the supplemental material.
Revised: The proof can be found in Section A of the supplemental material.

Original: Robust prediction.
Revised: Robust prediction.

Original: Recall that the server has learned a classifier f θ 0 during the training.
Revised: Recall that the server has learned a classifier f θ 0 during the training.

Original: We canfurther smooth this classifier f θ 0 to get f s (e.g., via Randomized Smoothing (Cohen et al., 2019;
Revised: We canfurther smooth this classifier f θ 0 to get f s (e.g., via Randomized Smoothing (Cohen et al., 2019;

Original: Lecuyer et al., 2019; Li et al., 2018))
Revised: Lecuyer et al., 2019; Li et al., 2018))

Original: such that f s ( D ϕ ( E ψ (ˆ l )))
Revised: such that f s ( D ϕ ( E ψ (ˆ l )))

Original: =
Revised: =

Original: f s ( D ϕ
Revised: f s ( D ϕ

Original: ( E ψ (ˆ l ))
Revised: ( E ψ (ˆ l ))

Original: + v ) for ∀∥ v ∥ 2 ≤∆ .
Revised: + v ) for ∀∥ v ∥ 2 ≤∆ .

Original: As we have ∥D ϕ ( E ψ (ˆ l ))
Revised: As we have ∥D ϕ ( E ψ (ˆ l ))

Original: − D ϕ
Revised: − D ϕ

Original: ( E ψ ( l ∗ ))
Revised: ( E ψ ( l ∗ ))

Original: ∥ 2 ≤ ∆ , then it is guaranteed that f s ( D ϕ ( E ψ (ˆ l )))
Revised: ∥ 2 ≤ ∆ , then it is guaranteed that f s ( D ϕ ( E ψ (ˆ l )))

Original: =
Revised: =

Original: f s ( D ϕ
Revised: f s ( D ϕ

Original: ( E ψ ( l ∗ ))) .
Revised: ( E ψ ( l ∗ ))) .

Original: Remark 5.
Revised: Remark 5.

Original: The condition (cid:80) i ∈
Revised: The condition (cid:80) i ∈

Original: S Ω
Revised: S Ω

Original: ∥ v i ∥ 2 − (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 > 2 δ required by Theorem 4 is stronger thanthe condition (cid:80) i ∈
Revised: ∥ v i ∥ 2 − (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 > 2 δ required by Theorem 4 is stronger thanthe condition (cid:80) i ∈

Original: Ω
Revised: Ω

Original: ∥ v i ∥ 2 > 2 δ required by Theorem 3.
Revised: ∥ v i ∥ 2 > 2 δ required by Theorem 3.

Original: This is due to the presence of corruptionsin the observed blocks.
Revised: This is due to the presence of corruptionsin the observed blocks.

Original: As discussed in Remark 4, when ∥ v ∥ 2 > ∆ , we have (cid:80) Mi
Revised: As discussed in Remark 4, when ∥ v ∥ 2 > ∆ , we have (cid:80) Mi

Original: =1 ∥ v i ∥ 2 > ∆ .
Revised: =1 ∥ v i ∥ 2 > ∆ .

Original: Again, we prefer v to be not ‘spiky’, thus (cid:80) i ∈ Ω ∥ v i ∥ 2 can have a significant portion of massfrom (cid:80) Mi
Revised: Again, we prefer v to be not ‘spiky’, thus (cid:80) i ∈ Ω ∥ v i ∥ 2 can have a significant portion of massfrom (cid:80) Mi

Original: =1 ∥ v i ∥ 2 when the number of observed blocks | Ω | is large.
Revised: =1 ∥ v i ∥ 2 when the number of observed blocks | Ω | is large.

Original: Further, when the numberof corrupted blocks q = | ¯ S Ω | is small enough, we can have (cid:80) i ∈
Revised: Further, when the numberof corrupted blocks q = | ¯ S Ω | is small enough, we can have (cid:80) i ∈

Original: S Ω
Revised: S Ω

Original: ∥ v i ∥ 2 − (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 > 2 δ .
Revised: ∥ v i ∥ 2 − (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 > 2 δ .

Original: To draw the connections to the Range Space Property in robust linear regression, we continueon the discussions in Remark 2.
Revised: To draw the connections to the Range Space Property in robust linear regression, we continueon the discussions in Remark 2.

Original: Let’s say (cid:80) i ∈
Revised: Let’s say (cid:80) i ∈

Original: Ω
Revised: Ω

Original: ∥ v i ∥ 2 = ∆ ′ , then a sufficient condition for 243 (cid:80) i ∈ S Ω
Revised: ∥ v i ∥ 2 = ∆ ′ , then a sufficient condition for 243 (cid:80) i ∈ S Ω

Original: ∥ v i ∥ 2 − (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 > 2 δ to hold is [ c Ω q ( W end ) − (1 − c Ω q ( W end ))] > 2 δ/ ∆ ′ .
Revised: ∥ v i ∥ 2 − (cid:80) i ∈ ¯ S Ω ∥ v i ∥ 2 > 2 δ to hold is [ c Ω q ( W end ) − (1 − c Ω q ( W end ))] > 2 δ/ ∆ ′ .

Original: Intuitively,the smaller number q of corrupted blocks and smaller δ ( i.e. , better recovered feature subspace) aswell as larger observed set Ω (then ∆ ′ approaches ∆ ), can lead to smaller recovery error bound ∆ .
Revised: Intuitively,the smaller number q of corrupted blocks and smaller δ ( i.e. , better recovered feature subspace) aswell as larger observed set Ω (then ∆ ′ approaches ∆ ), can lead to smaller recovery error bound ∆ .

Original:  Related work and discussion
Revised:  Related work and discussion

Original: Handling missing data and outliers in IoT. Sanyal & Zhang (2018) model the underlying uncorrupted IoT data as a low-dimensional linear subspace, i.e. , low-rank, and try to clean the corruptedsensor data by projecting them onto the estimated linear subspace.
Revised: Handling missing data and outliers in IoT. Sanyal & Zhang (2018) model the underlying uncorrupted IoT data as a low-dimensional linear subspace, i.e. , low-rank, and try to clean the corruptedsensor data by projecting them onto the estimated linear subspace.

Original: There is no guarantee of recoveringthe underlying uncorrupted sensor data.
Revised: There is no guarantee of recoveringthe underlying uncorrupted sensor data.

Original: This approach is a linear version of the manifold projectionmethod that we will discuss in detail.
Revised: This approach is a linear version of the manifold projectionmethod that we will discuss in detail.

Original: Kekatos & Giannakis (2011) studied the robust sensing problem where each sensor i has a linearmeasurement of the unknown parameter x , i.e. , b i
Revised: Kekatos & Giannakis (2011) studied the robust sensing problem where each sensor i has a linearmeasurement of the unknown parameter x , i.e. , b i

Original: =
Revised: =

Original: A i x , where both b i and A i are known andsome sensors are additionally corrupted by large measurement errors, but without any missing values.
Revised: A i x , where both b i and A i are known andsome sensors are additionally corrupted by large measurement errors, but without any missing values.

Original: They showed that it is possible to recover the true parameter x despite outlier corruptions.
Revised: They showed that it is possible to recover the true parameter x despite outlier corruptions.

Original: They alsoconsider the scenario with dense inlier noise in the measurements, i.e. , where the measurements b ionly approximately lie on the range space of matrix
Revised: They alsoconsider the scenario with dense inlier noise in the measurements, i.e. , where the measurements b ionly approximately lie on the range space of matrix

Original: A i , but their corresponding method does nothave any recovery guarantee in such a noisy case.
Revised: A i , but their corresponding method does nothave any recovery guarantee in such a noisy case.

Original: There are also some works that study the scenario where the sensors observe different noisy versionsof the same data vector, and they show it is possible to find a set of unattacked sensors based on theassumption that fraction of attacked sensors is small, e.g., (Zhang et al., 2018) or utilizing additionalknowledge that some sensors are not attacked (Wilson & Veeravalli, 2016).
Revised: There are also some works that study the scenario where the sensors observe different noisy versionsof the same data vector, and they show it is possible to find a set of unattacked sensors based on theassumption that fraction of attacked sensors is small, e.g., (Zhang et al., 2018) or utilizing additionalknowledge that some sensors are not attacked (Wilson & Veeravalli, 2016).

Original: We refer the interestedreaders to the survey paper (Zhang et al., 2018) for additional details on this line of work.
Revised: We refer the interestedreaders to the survey paper (Zhang et al., 2018) for additional details on this line of work.

Original: Certified robustness for defending against adversarial attacks.
Revised: Certified robustness for defending against adversarial attacks.

Original: In the adversarial machine learningliterature, many defense methods have been proposed to deal with adversarial perturbations.
Revised: In the adversarial machine learningliterature, many defense methods have been proposed to deal with adversarial perturbations.

Original: Werefer the reader to Chakraborty et al.
Revised: Werefer the reader to Chakraborty et al.

Original: (2018) for a comprehensive survey.
Revised: (2018) for a comprehensive survey.

Original: However, most of theexisting defenses can only deal with small adversarial perturbations.
Revised: However, most of theexisting defenses can only deal with small adversarial perturbations.

Original: Even worse, as new defensesare proposed, corresponding new attacks are also developed to adaptively attack them.
Revised: Even worse, as new defensesare proposed, corresponding new attacks are also developed to adaptively attack them.

Original: Experimentaldemonstrations of a defense’s efficacy based on currently existing attacks do not provide a generalproof of security.
Revised: Experimentaldemonstrations of a defense’s efficacy based on currently existing attacks do not provide a generalproof of security.

Original: Therefore, certifiably robust defense are of great interest.
Revised: Therefore, certifiably robust defense are of great interest.

Original: Randomized Smoothing(RS) is a promising technique (Cohen et al., 2019; Lecuyer et al., 2019; Li et al., 2018) whichsmoothes a base classifier to get a robust classifier, such that the prediction of this robust classifer on adversarially perturbed input is the same as its prediction on the original input, when the ℓ p -normof the adversarial perturbation is sufficiently bounded.
Revised: Randomized Smoothing(RS) is a promising technique (Cohen et al., 2019; Lecuyer et al., 2019; Li et al., 2018) whichsmoothes a base classifier to get a robust classifier, such that the prediction of this robust classifer on adversarially perturbed input is the same as its prediction on the original input, when the ℓ p -normof the adversarial perturbation is sufficiently bounded.

Original: Unfortunately, in our setting, the adversarialperturbation on compromised devices’ embedded features can be very large.
Revised: Unfortunately, in our setting, the adversarialperturbation on compromised devices’ embedded features can be very large.

Original: Further, RS does nothave robustness guarantees when there are missing features.
Revised: Further, RS does nothave robustness guarantees when there are missing features.

Original: Another promising defense is Randomized Ablation (Levine & Feizi, 2020), which was proposed todefend against sparse adversarial attacks where a small number of pixels in an image are changed tosome arbitrary values in [0,1].
Revised: Another promising defense is Randomized Ablation (Levine & Feizi, 2020), which was proposed todefend against sparse adversarial attacks where a small number of pixels in an image are changed tosome arbitrary values in [0,1].

Original: We extend this to the collaborative inference setting for comparison:the FC learns to make predictions based on randomly selected subsets of agent features during thetraining; and in the inference phase, FC randomly select a subset of agents to make a prediction.
Revised: We extend this to the collaborative inference setting for comparison:the FC learns to make predictions based on randomly selected subsets of agent features during thetraining; and in the inference phase, FC randomly select a subset of agents to make a prediction.

Original: Thefinal prediction is by majority voting over the random trials.
Revised: Thefinal prediction is by majority voting over the random trials.

Original: However, it’s hard to design a baseclassifier that is able to train on randomly ablated input features 1 .
Revised: However, it’s hard to design a baseclassifier that is able to train on randomly ablated input features 1 .

Original: Another limitation of this method isthat its robust prediction (based on randomly ablated features) may not be the same as the attack-freevanilla setting ( i.e. , the prediction of the vanilla classifier on the uncorrupted data).
Revised: Another limitation of this method isthat its robust prediction (based on randomly ablated features) may not be the same as the attack-freevanilla setting ( i.e. , the prediction of the vanilla classifier on the uncorrupted data).

Original: Further, to choosehow many pixels to ablate, it needs the knowledge of the number of corrupted pixels.
Revised: Further, to choosehow many pixels to ablate, it needs the knowledge of the number of corrupted pixels.

Original: Manifold Projection vs. Feature Purification.
Revised: Manifold Projection vs. Feature Purification.

Original: In the centralized setting, several works (Meng &
Revised: In the centralized setting, several works (Meng &

Original: Chen, 2017; Ilyas et al., 2017; Lindqvist et al., 2018) proposed to project the adversarial perturbedimage onto the manifold of the normal data to defend against adversarial attacks.
Revised: Chen, 2017; Ilyas et al., 2017; Lindqvist et al., 2018) proposed to project the adversarial perturbedimage onto the manifold of the normal data to defend against adversarial attacks.

Original: A natural questionis: why do we propose a feature purification method, instead of using manifold projection?
Revised: A natural questionis: why do we propose a feature purification method, instead of using manifold projection?

Original: First,the manifold projection approach can not guarantee recovery of the underlying features.
Revised: First,the manifold projection approach can not guarantee recovery of the underlying features.

Original: In fact, theresulting projected features can be arbitrarily far from the underlying true features if the adversarialperturbation can be arbitrarily large.
Revised: In fact, theresulting projected features can be arbitrarily far from the underlying true features if the adversarialperturbation can be arbitrarily large.

Original: To make this concrete, consider the linear manifold, i.e. ,subspace.
Revised: To make this concrete, consider the linear manifold, i.e. ,subspace.

Original: Suppose the underlying true feature vector l is generated from the column space of ad by r tall matrix U .
Revised: Suppose the underlying true feature vector l is generated from the column space of ad by r tall matrix U .

Original: We observe the corrupted feature vector h , which can be decomposed asl + e , where e models the adversarial perturbations.
Revised: We observe the corrupted feature vector h , which can be decomposed asl + e , where e models the adversarial perturbations.

Original: Projecting h onto the column space of Uwill get P U
Revised: Projecting h onto the column space of Uwill get P U

Original: h
Revised: h

Original: = P U l + P U e =
Revised: = P U l + P U e =

Original: l + P U e ,
Revised: l + P U e ,

Original: where P U =
Revised: where P U =

Original: U ( U T U ) − 1 U T .
Revised: U ( U T U ) − 1 U T .

Original: So the recovery erroris P U
Revised: So the recovery erroris P U

Original: h − l = P U e .
Revised: h − l = P U e .

Original: Apparently, if the adversarial perturbation vector e is not orthogonal to thissubspace (which is often the case), the recovery error would be proportional to the magnitude of theperturbation!
Revised: Apparently, if the adversarial perturbation vector e is not orthogonal to thissubspace (which is often the case), the recovery error would be proportional to the magnitude of theperturbation!

Original: It’s hard to recover the underlying true feature even if we have a perfect subspace, andthe problem looks even more challenging when the underlying manifold is non-linear.
Revised: It’s hard to recover the underlying true feature even if we have a perfect subspace, andthe problem looks even more challenging when the underlying manifold is non-linear.

Original: Fortunately,in our collaborative inference setting, there is important prior information about the pattern ofthe corruptions that we can leverage: the perturbation e has a block-wise structure (each blockcorresponds to an agent), and the fraction of the perturbed blocks is small.
Revised: Fortunately,in our collaborative inference setting, there is important prior information about the pattern ofthe corruptions that we can leverage: the perturbation e has a block-wise structure (each blockcorresponds to an agent), and the fraction of the perturbed blocks is small.

Original: Therefore, e has ablock-sparse structure.
Revised: Therefore, e has ablock-sparse structure.

Original: By exploring this fact, our proposed feature purification method can provablyrecover the underlying feature vector exactly or with bounded error, even if the underlying manifoldis highly non-linear and the magnitude of the perturbation is arbitrarily large.
Revised: By exploring this fact, our proposed feature purification method can provablyrecover the underlying feature vector exactly or with bounded error, even if the underlying manifoldis highly non-linear and the magnitude of the perturbation is arbitrarily large.

Original:  Empirical evaluation
Revised:  Empirical evaluation

Original: We first describe experimental setup and introduce a new attack in Section 5.1, then present experimental results in Section 5.2.
Revised: We first describe experimental setup and introduce a new attack in Section 5.1, then present experimental results in Section 5.2.

Original: More results can be found in Section B of supplemental material.
Revised: More results can be found in Section B of supplemental material.

Original: 5.1 Experimental setup
Revised: 5.1 Experimental setup

Original: Datasets.
Revised: Datasets.

Original: We study the classification task on ExtraSensory (Vaizman et al., 2017) and NUSWIDE (Chua et al., 2009) datasets.
Revised: We study the classification task on ExtraSensory (Vaizman et al., 2017) and NUSWIDE (Chua et al., 2009) datasets.

Original: ExtraSensory contains the measurements from diverse sensorsof smart phone and smart watch.
Revised: ExtraSensory contains the measurements from diverse sensorsof smart phone and smart watch.

Original: We divide the sensors into 10 agents, which correspond to phoneaccelerometer, gyroscope, magnetometer, watch accelerometer, compass, location, audio, phonestates (e.g., battery, wifi, ringing mode), environment sensing (e.g., light, air pressure, humidity, andtemperature), and time-of-day (e.g., morning).
Revised: We divide the sensors into 10 agents, which correspond to phoneaccelerometer, gyroscope, magnetometer, watch accelerometer, compass, location, audio, phonestates (e.g., battery, wifi, ringing mode), environment sensing (e.g., light, air pressure, humidity, andtemperature), and time-of-day (e.g., morning).

Original: There are naturally missing-feature problems, e.g., theuser may not permit the use of microphone and/or location.
Revised: There are naturally missing-feature problems, e.g., theuser may not permit the use of microphone and/or location.

Original: The original purpose of this dataset isto recognize the behavioral context of the user (e.g., sitting, walking, and running) and recommendmore appropriate music.
Revised: The original purpose of this dataset isto recognize the behavioral context of the user (e.g., sitting, walking, and running) and recommendmore appropriate music.

Original: We use first 1721 samples from a user for training, and the rest 465 samplesfor testing, with the binary label ’sitting’ or not.
Revised: We use first 1721 samples from a user for training, and the rest 465 samplesfor testing, with the binary label ’sitting’ or not.

Original: In NUS-WIDE, each sample has 634 image features, 1000 text features, and 5 different labels, i.e. ,‘buildings’, ‘grass’, ‘animal’, ‘water’, ‘person’.
Revised: In NUS-WIDE, each sample has 634 image features, 1000 text features, and 5 different labels, i.e. ,‘buildings’, ‘grass’, ‘animal’, ‘water’, ‘person’.

Original: We split the features into 4 agents, where the 1st agent holds 360-d image features, 2nd agent holds rest of image features, and the remaining 2 agentseach hold 500-d text features.
Revised: We split the features into 4 agents, where the 1st agent holds 360-d image features, 2nd agent holds rest of image features, and the remaining 2 agentseach hold 500-d text features.

Original: We use 60000 samples for training, 1000 samples for testing targetedattack, and 10000 samples for testing untargeted attack.
Revised: We use 60000 samples for training, 1000 samples for testing targetedattack, and 10000 samples for testing untargeted attack.

Original: Attack Setup.
Revised: Attack Setup.

Original: We perform the inference adversarial attacks, as well as its combination with missingfeature attack (missing-feature attack alone is not very effective).
Revised: We perform the inference adversarial attacks, as well as its combination with missingfeature attack (missing-feature attack alone is not very effective).

Original: For the adversarial attack, we testboth targeted and untargeted attacks.
Revised: For the adversarial attack, we testboth targeted and untargeted attacks.

Original: For the targeted attack , we adopt the commonly used Projected Gradient Descent (PGD) attackto generate adversarial perturbations on the embedded features held by malicious agents.
Revised: For the targeted attack , we adopt the commonly used Projected Gradient Descent (PGD) attackto generate adversarial perturbations on the embedded features held by malicious agents.

Original: However,since in this collaborative inference setting, the magnitude of the perturbation on the embeddedfeatures is allowed to be very large, we omit the projection step in PGD (or one can think it asprojecting onto a very large norm ball as the maximum perturbation constraint).
Revised: However,since in this collaborative inference setting, the magnitude of the perturbation on the embeddedfeatures is allowed to be very large, we omit the projection step in PGD (or one can think it asprojecting onto a very large norm ball as the maximum perturbation constraint).

Original: In the ExtraSensorydataset, the target label is simply the opposite of the true label, since it is binary classification.
Revised: In the ExtraSensorydataset, the target label is simply the opposite of the true label, since it is binary classification.

Original: We set the PGD attack with learning rate 0.5 and 30 iterations, so that it can successfully attackthe unsecured model.
Revised: We set the PGD attack with learning rate 0.5 and 30 iterations, so that it can successfully attackthe unsecured model.

Original: While in the NUS-WIDE dataset, the target label is set to be ‘grass’.
Revised: While in the NUS-WIDE dataset, the target label is set to be ‘grass’.

Original: Weset the PGD attack with learning rate 0.1 and 50 iterations.
Revised: Weset the PGD attack with learning rate 0.1 and 50 iterations.

Original: Note that PGD attack is a whitebox attack, where the attacker tries to find arg min h adv ℓ ( f θ 

Revised: Note that PGD attack is a whitebox attack, where the attacker tries to find arg min h adv ℓ ( f θ

Original: ( { h adv , h benign } ) , y A ) via using thegradient dℓ ( f θ 

Revised: ( { h adv , h benign } ) , y A ) via using thegradient dℓ ( f θ

Original: { h adv , h benign } ) , y A ) /d
Revised: { h adv , h benign } ) , y A ) /d

Original: h adv , where h adv denotes the overall embedded featuresheld by malicious agents and
Revised: h adv , where h adv denotes the overall embedded featuresheld by malicious agents and

Original: y A is the target label.
Revised: y A is the target label.

Original: In practice, the attacker may not easily obtainsuch gradient information, which usually needs to know the trained global model f
Revised: In practice, the attacker may not easily obtainsuch gradient information, which usually needs to know the trained global model f

Original: θ
Revised: θ

Original: and h benign .
Revised: and h benign .

Original: Nevertheless, this attack serves as a smart targeted perturbation on the embedded features to test therobustness of the defense methods.
Revised: Nevertheless, this attack serves as a smart targeted perturbation on the embedded features to test therobustness of the defense methods.

Original: Motivated by the discussions in Section 4, we propose and test an untargeted attack , which isagnostic to the model, training data, label, as well as the features held by other agents.
Revised: Motivated by the discussions in Section 4, we propose and test an untargeted attack , which isagnostic to the model, training data, label, as well as the features held by other agents.

Original: We call itdistributed feature-flipping attack .
Revised: We call itdistributed feature-flipping attack .

Original: In this attack, the malicious agent(s) simply flips the sign of itsembedded feature vector l i and further amplifies its magnitude.
Revised: In this attack, the malicious agent(s) simply flips the sign of itsembedded feature vector l i and further amplifies its magnitude.

Original: Formally, during this inference phaseuntargeted attack, the malicious agent i provides the embedded features h
Revised: Formally, during this inference phaseuntargeted attack, the malicious agent i provides the embedded features h

Original: i
Revised: i

Original: =
Revised: =

Original: − Amplification × l ito the FC, instead of providing the true embedded features l i .
Revised: − Amplification × l ito the FC, instead of providing the true embedded features l i .

Original: This attack can be easily understoodin the binary classification with linear classifer, since flipping the sign of the whole feature vectorwill likely flip the sign of the predicted label as well.
Revised: This attack can be easily understoodin the binary classification with linear classifer, since flipping the sign of the whole feature vectorwill likely flip the sign of the predicted label as well.

Original: To illustrate the effectiveness of this attackagainst the manifold-projection based defenses, we can look it as adding the corruption vectore i = − (1 + Amplification ) × l i to the true embedded feature vector l i of i -th agent, which ismalicious.
Revised: To illustrate the effectiveness of this attackagainst the manifold-projection based defenses, we can look it as adding the corruption vectore i = − (1 + Amplification ) × l i to the true embedded feature vector l i of i -th agent, which ismalicious.

Original: So the overall perturbation vector e has many zero blocks corresponding to benignagents.
Revised: So the overall perturbation vector e has many zero blocks corresponding to benignagents.

Original: Its non-zero blocks have values e i = − (1 + Amplification )
Revised: Its non-zero blocks have values e i = − (1 + Amplification )

Original: × l i , ∀ i corresponds to themalicious agents.
Revised: × l i , ∀ i corresponds to themalicious agents.

Original: So the inner product between perturbation vector e and true embedded featuresl is − (1 + Amplification ) (cid:80) i ∈
Revised: So the inner product between perturbation vector e and true embedded featuresl is − (1 + Amplification ) (cid:80) i ∈

Original: Ω adv ∥ l i ∥ 22 .
Revised: Ω adv ∥ l i ∥ 22 .

Original: Such perturbation vector e introduces correlations withthe underlying manifold of the uncorrupted embedded features.
Revised: Such perturbation vector e introduces correlations withthe underlying manifold of the uncorrupted embedded features.

Original: Therefore, projecting the corruptedembedded features h onto the manifold is hard to get rid of such corruptions.
Revised: Therefore, projecting the corruptedembedded features h onto the manifold is hard to get rid of such corruptions.

Original: We will see suchfeature-flipping attack is very effective in attacking many existing defenses.
Revised: We will see suchfeature-flipping attack is very effective in attacking many existing defenses.

Original: Baseline methods.
Revised: Baseline methods.

Original: We compare with 1)
Revised: We compare with 1)

Original: Unsecured : no defense.
Revised: Unsecured : no defense.

Original: 2) Manifold Projection : project thecorrupted embedded features h test onto the learned manifold via Autoencoder, i.e. , D ϕ ( E ψ ( h test )) .3)
Revised: 2) Manifold Projection : project thecorrupted embedded features h test onto the learned manifold via Autoencoder, i.e. , D ϕ ( E ψ ( h test )) .3)

Original: Randomized Smoothing (Cohen et al., 2019), where randomly generated Gaussian noise is addedto h test for the FC to make the prediction, the final prediction is by majority voting over 1000 suchrandom trials.
Revised: Randomized Smoothing (Cohen et al., 2019), where randomly generated Gaussian noise is addedto h test for the FC to make the prediction, the final prediction is by majority voting over 1000 suchrandom trials.

Original: Randomized Smoothing Block : since only some block(s) of the embedded featuresare corrupted, we further compare with an oracle version of Randomized Smoothing, which only addsrandom Gaussian noise to the corrupted blocks of h test .
Revised: Randomized Smoothing Block : since only some block(s) of the embedded featuresare corrupted, we further compare with an oracle version of Randomized Smoothing, which only addsrandom Gaussian noise to the corrupted blocks of h test .

Original: Randomized Ablation (Levine & Feizi,2020) 6)
Revised: Randomized Ablation (Levine & Feizi,2020) 6)

Original: Adversarial Training (AT) (Madry et al., 2018): standard adversarial training via using
Revised: Adversarial Training (AT) (Madry et al., 2018): standard adversarial training via using

Original: PGD attacks on h train to generate adversarial examples, but without using projection step to limit themagnitude of adversarial perturbations.
Revised: PGD attacks on h train to generate adversarial examples, but without using projection step to limit themagnitude of adversarial perturbations.

Original: For defending against the targeted attack, it knows the targetlabel and leverages that to generate targeted adversarial examples.
Revised: For defending against the targeted attack, it knows the targetlabel and leverages that to generate targeted adversarial examples.

Original: For defending against untargetedattack, it uses untargeted PGD attack to generate adversarial examples.
Revised: For defending against untargetedattack, it uses untargeted PGD attack to generate adversarial examples.

Original: 7) Block-wise Adversarial
Revised: 7) Block-wise Adversarial

Original: Training (BAT) : since only some block(s) of h test will be corrupted, we further propose and test ablock-wise version of AT: for each training sample’s embedded features, we randomly select a blockand generate adversarial perturbations (via corresponding targeted or untargeted PGD attack) on thatblock for adversarial training.
Revised: Training (BAT) : since only some block(s) of h test will be corrupted, we further propose and test ablock-wise version of AT: for each training sample’s embedded features, we randomly select a blockand generate adversarial perturbations (via corresponding targeted or untargeted PGD attack) on thatblock for adversarial training.

Original: 8) Oracle : where there is no attack.
Revised: 8) Oracle : where there is no attack.

Original: The implementation details of thebaselines and the proposed CoPur can be found in Section C of the supplemental material.
Revised: The implementation details of thebaselines and the proposed CoPur can be found in Section C of the supplemental material.

Original: 5.2 Empirical results
Revised: 5.2 Empirical results

Original: We report the Robust Accuracy of each defense method, i.e. , classification accuracy on the adversarialcorrupted embedded features, against distributed adversarial attack and its combination with missingfeature attack.
Revised: We report the Robust Accuracy of each defense method, i.e. , classification accuracy on the adversarialcorrupted embedded features, against distributed adversarial attack and its combination with missingfeature attack.

Original: More experiments can be found in Section B of the supplemental material.
Revised: More experiments can be found in Section B of the supplemental material.

Original: A) Adversarial attack.
Revised: A) Adversarial attack.

Original: We first test the robustness of the defense methods under the targeted attack.
Revised: We first test the robustness of the defense methods under the targeted attack.

Original: Table 1 shows the Robust Accuracy of each method against targeted PGD attacks on NUS-WIDE and
Revised: Table 1 shows the Robust Accuracy of each method against targeted PGD attacks on NUS-WIDE and

Original: ExtraSensory datasets, where the last agent is malicious.
Revised: ExtraSensory datasets, where the last agent is malicious.

Original: The Manifold Projection method (whichhas no recovery guarantee) shows some resistance on the NUS-WIDE dataset, but is not robust on
Revised: The Manifold Projection method (whichhas no recovery guarantee) shows some resistance on the NUS-WIDE dataset, but is not robust on

Original: ExtraSensory dataset.
Revised: ExtraSensory dataset.

Original: The proposed method achieves the best Robust Accuracy on both datasets.
Revised: The proposed method achieves the best Robust Accuracy on both datasets.

Original: In Figure 2a, we report the Robust Accuracy of the defense methods under the untargeted featureflipping attack on the ExtraSensory dataset, with Amplification ranging from [1, 3, 5, 10, 20], and thelast 3 agents perform such attack.
Revised: In Figure 2a, we report the Robust Accuracy of the defense methods under the untargeted featureflipping attack on the ExtraSensory dataset, with Amplification ranging from [1, 3, 5, 10, 20], and thelast 3 agents perform such attack.

Original: The Robust Accuracies of the compared methods drop significantlywhen the Amplification gets larger.
Revised: The Robust Accuracies of the compared methods drop significantlywhen the Amplification gets larger.

Original: The proposed method significantly outperforms the baselineswith a large margin.
Revised: The proposed method significantly outperforms the baselineswith a large margin.

Original: [Table]
Revised: [Table]

Original: combined attacks on ExtraSensory dataset with 10 agents: (a) Agents 8,9,10 provide corrupted embedded features h 8 , 9 , 10 = − Amplification × l 8 , 9 , 10 .
Revised: combined attacks on ExtraSensory dataset with 10 agents: (a) Agents 8,9,10 provide corrupted embedded features h 8 , 9 , 10 = − Amplification × l 8 , 9 , 10 .

Original: (b) Agent 7 performs missing-feature attack, while agent 10 provides corrupted embedded features h 10 =
Revised: (b) Agent 7 performs missing-feature attack, while agent 10 provides corrupted embedded features h 10 =

Original: − Amplification × l 10 .
Revised: − Amplification × l 10 .

Original: 393 B) Combined attack.
Revised: 393 B) Combined attack.

Original: We now test the combined attack on ExtraSensory dataset, where agent 7which holds audio MFCC features, performs missing-feature attack, and agent 10 performs untargeted(Figure 2b) or targeted (Table 2) adversarial attack.
Revised: We now test the combined attack on ExtraSensory dataset, where agent 7which holds audio MFCC features, performs missing-feature attack, and agent 10 performs untargeted(Figure 2b) or targeted (Table 2) adversarial attack.

Original: The proposed method significantly outperformsthe baseline methods and maintains very high Robust Accuracy under both attacks.
Revised: The proposed method significantly outperformsthe baseline methods and maintains very high Robust Accuracy under both attacks.

Original: [Table]
Revised: [Table]

Original:  Conclusions
Revised:  Conclusions

Original: In this work, we proposed a novel feature purification based robust collaborative inference frameworkto defend against a variety of attacks during inference, with theoretical guarantees.
Revised: In this work, we proposed a novel feature purification based robust collaborative inference frameworkto defend against a variety of attacks during inference, with theoretical guarantees.

Original: The proposednon-linear robust decomposition method and its theoretical analyses may have much wider impacts.
Revised: The proposednon-linear robust decomposition method and its theoretical analyses may have much wider impacts.

Original: We further validate the robustness of the proposed framework through extensive experiments on
Revised: We further validate the robustness of the proposed framework through extensive experiments on

Original: ExtraSensory and NUS-WIDE datasets.
Revised: ExtraSensory and NUS-WIDE datasets.

Original: One of our future work is to extend Algorithm 1 of Sharonet al.
Revised: One of our future work is to extend Algorithm 1 of Sharonet al.

Original: (2009) to calculate our block-wise incomplete leverage constant introduced in Remark 2.
Revised: (2009) to calculate our block-wise incomplete leverage constant introduced in Remark 2.

Original: Checklist
Revised: Checklist

Original: [Table]
Revised: [Table]

Original: For all authors...
Revised: For all authors...

Original: [Table]
Revised: [Table]

Original: (a) Do the main claims made in the abstract and introduction accurately reflect the paper’scontributions and scope?
Revised: (a) Do the main claims made in the abstract and introduction accurately reflect the paper’scontributions and scope?

Original: [Yes]
Revised: [Yes]

Original: [Table]
Revised: [Table]

Original: (b) Did you describe the limitations of your work?
Revised: (b) Did you describe the limitations of your work?

Original: [Yes] Please see Section E of supplemental material.
Revised: [Yes] Please see Section E of supplemental material.

Original: [Table]
Revised: [Table]

Original: (c) Did you discuss any potential negative societal impacts of your work?
Revised: (c) Did you discuss any potential negative societal impacts of your work?

Original: [Yes] Please seeSection E of supplemental material.
Revised: [Yes] Please seeSection E of supplemental material.

Original: [Table]
Revised: [Table]

Original: (d) Have you read the ethics review guidelines and ensured that your paper conforms to
Revised: (d) Have you read the ethics review guidelines and ensured that your paper conforms to

Original: [Table]
Revised: [Table]

Original: them?
Revised: them?

Original: [Yes]
Revised: [Yes]

Original: [Table]
Revised: [Table]

Original: If you are including theoretical results...
Revised: If you are including theoretical results...

Original: [Table]
Revised: [Table]

Original: (a) Did you state the full set of assumptions of all theoretical results?
Revised: (a) Did you state the full set of assumptions of all theoretical results?

Original: [Yes] Please seeSection
Revised: [Yes] Please seeSection

Original: [Table]
Revised: [Table]

Original: (b) Did you include complete proofs of all theoretical results?
Revised: (b) Did you include complete proofs of all theoretical results?

Original: [Yes] Please see Section Aof supplemental material.
Revised: [Yes] Please see Section Aof supplemental material.

Original: [Table]
Revised: [Table]

Original: If you ran experiments...
Revised: If you ran experiments...

Original: [Table]
Revised: [Table]

Original: (a) Did you include the code, data, and instructions needed to reproduce the main exper-
Revised: (a) Did you include the code, data, and instructions needed to reproduce the main exper-

Original: [Table]
Revised: [Table]

Original: imental results (either in the supplemental material or as a URL)?
Revised: imental results (either in the supplemental material or as a URL)?

Original: [Yes] Please seeSection C of supplemental material.
Revised: [Yes] Please seeSection C of supplemental material.

Original: [Table]
Revised: [Table]

Original: (b) Did you specify all the training details (e.g., data splits, hyperparameters, how theywere chosen)?
Revised: (b) Did you specify all the training details (e.g., data splits, hyperparameters, how theywere chosen)?

Original: [Yes] Please see Section C of supplemental material.
Revised: [Yes] Please see Section C of supplemental material.

Original: [Table]
Revised: [Table]

Original: (c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)?
Revised: (c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)?

Original: [Yes](d) Did you include the total amount of compute and the type of resources used (e.g.,type of GPUs, internal cluster, or cloud provider)?
Revised: [Yes](d) Did you include the total amount of compute and the type of resources used (e.g.,type of GPUs, internal cluster, or cloud provider)?

Original: [Yes] Please see Section C ofsupplemental material.4.
Revised: [Yes] Please see Section C ofsupplemental material.4.

Original: If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...
Revised: If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...

Original: (a)
Revised: (a)

Original: If your work uses existing assets, did you cite the creators?
Revised: If your work uses existing assets, did you cite the creators?

Original: [Yes]
Revised: [Yes]

Original: [Table]
Revised: [Table]

Original: (b) Did you mention the license of the assets?
Revised: (b) Did you mention the license of the assets?

Original: [Yes] License of ExtraSensory dataset is CCBY-NC-SA 4.0, but we can not find license for NUS-WIDE dataset.
Revised: [Yes] License of ExtraSensory dataset is CCBY-NC-SA 4.0, but we can not find license for NUS-WIDE dataset.

Original: (c) Did you include any new assets either in the supplemental material or as a URL?
Revised: (c) Did you include any new assets either in the supplemental material or as a URL?

Original: [Yes]
Revised: [Yes]

Original: (d) Did you discuss whether and how consent was obtained from people whose data you’re
Revised: (d) Did you discuss whether and how consent was obtained from people whose data you’re

Original: using/curating?
Revised: using/curating?

Original: [Yes] Please see Section C of supplemental material.
Revised: [Yes] Please see Section C of supplemental material.

Original: (e) Did you discuss whether the data you are using/curating contains personally identifiable
Revised: (e) Did you discuss whether the data you are using/curating contains personally identifiable

Original: information or offensive content?
Revised: information or offensive content?

Original: [Yes] Please see Section C of supplemental material.
Revised: [Yes] Please see Section C of supplemental material.

Original: If you used crowdsourcing or conducted research with human subjects...
Revised: If you used crowdsourcing or conducted research with human subjects...

Original: (a) Did you include the full text of instructions given to participants and screenshots, if
Revised: (a) Did you include the full text of instructions given to participants and screenshots, if

Original: applicable?
Revised: applicable?

Original: [N/A]
Revised: [N/A]

Original: (b) Did you describe any potential participant risks, with links to Institutional Review
Revised: (b) Did you describe any potential participant risks, with links to Institutional Review

Original: Board (IRB) approvals, if applicable?
Revised: Board (IRB) approvals, if applicable?

Original: [N/A]
Revised: [N/A]

Original: (c) Did you include the estimated hourly wage paid to participants and the total amount
Revised: (c) Did you include the estimated hourly wage paid to participants and the total amount

Original: spent on participant compensation?
Revised: spent on participant compensation?

Original: [N/A]
Revised: [N/A]