Federated Adversarial Domain Adaptation
Authors: Xingchao Peng
Zijun Huang
Yizhe Zhu
Kate Saenko
Venue:  (2020)
Keywords: Federated Learning
Domain Adaptation
Transfer Learning
Feature Disentanglement

Abstract: Federated learning improves data privacy and efficiency in machine learning performed over networks of distributed devices, such as mobile phones, IoT and wearable devices, etc. Yet models trained with federated learning can still fail to generalize to new devices due to the problem of domain shift. Domain shift occurs when the labeled data collected by source nodes statistically differs from the target node's unlabeled data. In this work, we present a principled approach to the problem of federated domain adaptation, which aims to align the representations learned among the different nodes with the data distribution of the target node. Our approach extends adversarial adaptation techniques to the constraints of the federated setting. In addition, we devise a dynamic attention mechanism and leverage feature disentanglement to enhance knowledge transfer. Empirically, we perform extensive experiments on several image and text classification tasks and show promising results under unsupervised federated domain adaptation setting.

Published as a conference paper at ICLR 2020
FEDERATED ADVERSARIAL DOMAIN ADAPTATION
Xingchao Peng
Zijun Huang
Boston University
Columbia University
Boston, MA 02215, USA xpeng@bu. edu
New York City, NY 10027, USA
zi jun www.ghcalnnnnann.oe umbia edu
Yizhe Zhu
Kate Saenko
Rutgers University Piscataway, NJ 08854, USA
Boston University
Boston, MA 02215, USA saenko bu edu
yz.500sscar ceemmaaareaaaa.a 1 rutgers, edu
ABSTRACT
Federated learning improves data privacy and efficiency in machine learning per- formed over networks of distributed devices, such as mobile phones, 1oT and wearable devices, etc. Yet models trained with federated learning can still fail to generalize to new devices due to the problem of domain shift. Domain shift occurs when the labeled data collected by source nodes statistically differs from the target node's unlabeled data. In this work, we present a principled approach to the problem of federated domain adaptation, which aims to align the represen- tations learned among the different nodes with the data distribution of the target node. Our approach extends adversarial adaptation techniques to the constraints the federated setting. In addition, we devise dynamic attention mechanism and leverage feature disentanglement to enhance knowledge transfer. Empirically, we perform extensive experiments several image and text classification tasks and show promising results under unsupervised federated domain adaptation setting.
INTRODUCTION
Data generated by networks of mobile and loT devices poses unique challenges for training machine learning models. Due to the growing storage/computationn power of these devices and concerns about data privacy, it is increasingly attractive to keep data and computation locally on the device (Smith et al. 2017). Federated Learning (FL) (Mohassel & Rindal, 2018: Bonawitz et al. 2017) Mohassel wwwaacyyreeeeiinggg. mechanism to leverage such decen- decen-tralized tralized data and computation resources to train machine learning models. The main idea behind federated learning is to have each node learn on its own local data and not share either the data or the model parameters.
& Zhang 2017) provides
While federated learning promises better privacy and efficiency, existing methods ignore the fact that the data on each node are collected in non-i.idd manner, leading domain shift between nodes (Quionero-Candel) et al.) 2009). For example, one device may take photos mostly indoors, while another mostly outdoors. In this paper, we address the problem of transferring knowledge from the decentralized nodes to a new node with a different data domain, without requiring any additional supervision from the user. We define this novel problem Unsupervised Federated Domain Adaptation (UFDA), as illustrated in Figure ((a)
There a large body of existing work on unsupervised domain adaptation (Long et al. 2015 Ganin
& Lempitsky 2015] Tzeng et al.) 2017 Zhu et al. 2017 Gong et al. 2012 Long et al. 2018). but the federated setting presents several additional challenges. First, the data are stored locally and cannot be shared, which hampers mainstream domain adaptation methods as they need to access both the
labeled source and unlabeled target data (Tzeng et al. 2014 Long et al. 2017 Ghifary et al. 2016 Sun & Saenko 2016 Ganin & Lempitsky 2015 T Zzeng et al. (017). Second, the model parameters
are trained separately for each node and converge at different speeds, while also offering different contributions to the target node depending how close the two domains are. Finally, the knowledge

---

Published as a conference paper at ICLR 2020
Data
LesET
Tash Loos
Domala slouffin
Model Gradiou
Class ilenteer
Dimniangeer
R
Aggregated Gradient with Dynamte Attention
Domtin
Domuttrspeeife
Chace-aaimy
funnr
Source/lager
G2
Adversriist Alggesson
"Mane Mare-biigg
digenne
S1
SN
Dusentangis
(a) Federated Domain Adaptation
(b) Federated Adversarial Domain Adaptation
Figure 1: (a) We propose an approach for the UFDA setting. where data are not shareable between different domains. In our approach, models are trained separately on each source domain and their gradients are aggregated with dynamic attention mechanism to update the target model. (h) Our FADA model learns to extract domain-invariant features using adversarial domain alignment (red lines) and feature disentangler (blue lines).
learned from source nodes is highly entangled (Bengio et al. 2013). which can possibly lead to negative transfer (Pan & Yang 2010).
In this paper, we propose solution to the above problems called Federated Adversarial Domain Adaptation (FADA) which aims to tackle domain shift in federated learning system through adversarial techniques. Our approach preserves data privacy by training one model per source node and updating the target model with the aggregation of source gradients, but does so in way that reduces domain shift. First, we analyze the federated domain adaptation problem from a theoretical perspective and provide generalization bound. Inspired by our theoretical results, propose efficient adaptation algorithm based on adversarial adaptation and representation disentanglement applied to the federated setting. We also devise dynamic attention model cope with the varying convergence rates in the federated learning system. We conduct extensive experiments on real- world datasets, including image recognition and natural language tasks. Compared to bascline methods, we improve adaptation performance on all tasks, demonstrating the effectiveness of our devised model.
RELATED WORK
Unsupervised Domain Adaptation Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge learned from a labeled source domain an unlabeled target domain. Domain adaptation approaches proposed over the past decade include discrepancy- based methods (Tzeng et al. 2014
Long et al. 2017 Ghifary et al. 2014,,uun & Saenko 2016 Peng Saenko 2018), reconstruction-
based UDA models (Yi et al. 2017][Zhu et al.] 2017 Hoffman et al. 2018 Kim et al. (017). and
adversary-base bassed approaches (Luu Tuzel | zeng et al. [017]] Liu et al., [018a] Ganin &
Lempitsky 2015). For example, Ganin & Lempitsky (2015) propose gradient reversal layer to
perform adversarial training to a domain discriminator, inspired by the idea of adversarial learning.
Tzeng al. (2017) address unsupervised domain adaptation by adapting deep CNN-based feature cxtractor/classifier across source and target domains via adversarial training. [Ben-David et al. (2010) introduce HAA-divvrgeece to evaluate the domain shift and provide generalization bound for domain adaptation. These methods assume the data are centralized on one server, limiting their applicability the distributed learning system.
Federated Learning Federated learning (Mohassel & Rindal 2018 Rivest et al. 1978 Bonawitz
al. 2017 Mohassel & Zhang 2017) is decentralized learning approach which enables multiple clients to col collaboratively learn a machine learning model while keeping the training data and model parameters on local devices. Inspired by Homomorphic Encryption (Rivest al.) 1978), Gilad Bachrach et al. (2016) propose CryptoNets to enhance the efficiency of data encryption, achieving higher federated learning performance. [Bonawitz al. (2017) introduce a secure aggregation scheme to update the machine learning models under their federated learning framework. Recently, Mohassel Zhang (2017) propose SecureML to support privacy- preeeceireeevinggg collaborative training in multi- client federated learning system. However, these methods mainly aim to learn single global model across the data and have no convergence guarantee, which limits their ability to deal with n...i.i.d data. To address the non-i.i.d data, (Smith al. (2017) introduce federated multi-task learning, which learns a separate model for each node, Liu et al. (2018b) propose semi supervised federated transfer setting. However, their models involve full or semi-supervision. The
learning in
2

---

Published as a conference paper at ICLR 2020
work proposed here is, to our best knowledge, the first federated learning framework to consider unsupervised domain adaptation.
Feature Disentanglement Deep neural networks are known extract features where multiple hidden factors are highly entangled. Learning disentangled representations can help remove irrelevant and domain-specific features and model only the relevant factors of data variation. To this end, recent work (Mathieu et al. 2016) Makhzani et al. 2016 Liu et al. 2018a Odena et al.] (017) explores the learning of interpretable representations using generative adversarial networks (GANs) (Goodfellow) et al. 2014) and variational autoencoders (VAEs) (Kingma & Welling 2013). Under the fully supervised setting, (Odena et al.) 2017) propose an auxiliary classifier GAN (AC-GAN) achieve representation disentanglement (Liu et al., 20188) introduce unified feature disentanglement
framework to learn domain- invaariinn features from data across different domains. (Kingma et al. 2014) also extend VAEs into the semi-supervised setting for representation disentanglement. (Lee) et al. 2018) propose to disentangle the features into domain-invariant content space and a domain- specific attributes space, producing diverse outputs without paired training data. Inspired by these works, we propose a method to disentangle the domain-invaritn features, using adversarial training process. In addition, we propose to minimize the mutual information between the domain-inven invariant features and domain-speciffe features to enhance the feature disentanglement.
features from domain-specific
3 GENERALIZATION BOUND FOR FEDERATED DOMAIN ADAPTATION
We first define the notation and review a typical theoretical error bound for single-source domain adaptation (Ben-David et al., 2007; Blitzer et al. 2008) devised by Ben-David et al. Then we describe our derived error bound for unsupervised federated domain adaptation. We mainly focus on the high-level interpretation of the error bound here and refer our readers to the appendix (see supplementary material) for proof details.
Notation. Let D
and and Dr denote source and target distribution on input space X' and a ground-truth
labeling function X' + {0, 1). A hypothesis is function h X - {0, 1} with the error w.r. the ground- trunt labeling function g: (s := Ex~Ds [[(xx) - g(x)]] We denote the risk and empirical risk of hypothesis h on Ds as (h) and (h). Similarly, the risk and empirical risk of h on Dr are denoted as er (h) and (h)) ). The H-divergence between two distributions 'D and D'' is defined as: (D, D*) = 2 sup AE (A) Prp (A)), where H is hypothesis class for input space X. and An denotes the collection of subsets of X that are the support of some hypothesis in H.
The symmetric difference space HAH is defined as: HAH == (h(x) @ h (x,,), h' E HH. (#: the XOR operation). We denote the optimal hypothesis that achieves the minimum risk on the source and the target as h² := arg minnee (h))) + er and the error of h² as À := cs(h") ) + er(h") Blitzer et al. (2007)) prove the following error bound on the target domain.
Theorem 1. Let H. be hypothesis space of CC-diiennsoon d and Ds. Dr be the empirical distribu- tion induced by samples of size m drawn from Ds and Dr. Then with probability at least 1 8 over the choice of samples, for each h E H.
er(h) S (h(h) + (Ds, Dr) + 4V
1 2
2d 2dlog(2m) + log(4/8)
+
m
Let Ds=(Ds, 1 and Dr (x} be N source domains and the target domain in a UFDA system, where Ds; ( In federated domain adaptation system, D3 is distributed on N nodes and the data are not shareable with each other in the training process. The classical domain adaptation algorithms aim to minimize the target risk (h) := (r(x.))>,, [h(x) * y). However, in a UFDA system, one model cannot directly get access to data stored on different nodes for security and privacy reasons. To address this issue, we propose learn separate models for each distributed source domain hg = (hs. ) The target hypothesis by is the aggregation of the parameters of hs, i.e. hr := Σn a,hs,, Va E N.,, EiE[N] a =1. 1. We can then derive the following error bound: Theorem 2. (Weighted error bound for federated domain adaptation). Let H be hypothesis class with VC-dimension d and (Ds, NN Dr be empirical distributions induced by sample of size
In this literature, the calligraphic DD denotes data distrihution, and italic D denotes domain discriminator.
3

---

Published as a conference paper at ICLR 2020
from each source domain and target domain in a federated learning system, respectively. Then, da N Ei=1 04 = with probability at least 8 over the choice of samples, for each h H.
Σ 2 an Dr) (()) + 44 2d + log(4/5)
er(hr) S Eg( Σ aghs,) SE[N]
Nm
iE[N]
(Ds,.ry Pr) divergonce
VC-Dimension Constraint
orfOr OR Souuce
where X is the risk of the optimal hypothesis the mixture of Ds. and T, and $ is the mixture of source samples with size Nm. duau(Ds, Dr) denotes the divergence between domain Se and T.
Comparison with Existing Bounds The bound in is extended from T and they are equivalent if only one source domain exists (N = 1). Mansour et al. (209) provide generalization bound for multiple-source domain adaptation, assuming that the target domain is a mixture of the N source domains. In contrast, in our error bound ( the target domain is assumed to be an novel domain, resulting in bound involving HAH discrepancy (Ben-David et al. (010)) and the VC-dimensional constraint (Vapnik& & Vapnik 1998). Blitzer et al. (2007b) propose generalization bound for semi- supervised multi-source domain adaptation, assuming that partial target labels are available. Our generalization bound is devised for unsupervised learning. Zhao et al. (2018) introduce classification and regression error bounds for multi-source domain adaptation. However, these error bounds assume that the multiple source and target domains exactly share the same hypothesis. In contrast, our error bound involves multiple hypotheses.
4 FEDERATED ADVERSARIAL DOMAIN ADAPTATION
The error bound in Theorem demonstrates the importance of the weight and the discrepancy duaH( (Ds, Dr-) in unsupervised federated domain adaptation. Inspired by this, we propose dynamic attention model to learn the weight a and federated adversarial alignment minimize the discrepancy between the source and target domains, as shown in Figure In addition, we leverage representation disentanglement extract Nomain-invarian representations to further enhance knowledge transfer.
Dynamic Attention In a federated domain adaptation system, the models on different nodes have different convergence rates. In addition, the domain shifts between the source domains and target domain are different, leading to phenomenon where some nodes may have no contribution or even negative transfer (Pan & Yang (010)) to the target domain. To address this issue, we propose dynamic attention, which is a mask on the gradients from source domains. The philosophy behind the dynamic attention to increase the weight those nodes whose gradients are beneficial to the target domain and limit the weight of those whose gradients are detrimental to the target domain. Specifically, we leverage the gap statistics (Tibshirani et al. (201)) to evaluate how well the target features f can be clustered with unsupervised clustering algorithms (K- (K-Means). Assuming we have & clusters, the gap statistics are computed as:
Σ 11 11 IJEC,
2n
where we have clusters C1 .2,... Ck. with Cp denoting the indices of observations in cluster r; and I, Intuitively, a smaller gap statistics value indicates the feature distribution has smaller intra- class variance. We measure the contribution of each source domain by the gap statistics gain between two consecutive iterations: Igain = 1. I (p indicating training step), denoting how much the clusters can improved before and after the target model is updated with the i-th source model's gra- dient. The mask on the gradients from source domains defined as Softmar(( tmaar(]aain .rgrin.................. ,..., ....!!a Tgain
Federated Adversarial A Alignment The performance of machine learning models degrades rapidly with the presence of domain discrepancy (Long et al. (2015). To address this issue, existing work (Hoff) man et al.) 2018 Tzeng et al. 2015)) proposes minimize the discrepancy with an adversarial training process. For example. Tzeng et al. (2015) proposes the domain confusion objective, under which the feature extractor is trained with cross- cross-entropy loss against a uniform distribution. However, these models require access to the source and target data simultaneously, which prohibitive in UFDA. In the federated setting, we have multiple source domains and the data are locally stored
4

---

Published as a conference paper at ICLR 2020
Algorithm 1 Federated Adversarial Domain Adaptation
Input: N source domains (3=[Ds; J a target domain D. = {x]] N feature extractors
(e (0,,,. ). N disentanglers (o") " _x..,.xx))..... N classifiers ( ,..eeex ). N class identifiers ( ...e",,,..NN ). N mutual information estimators (0 6
( [MM trained on domains. Target feature extractor 0 classifier @ N domain identifiers ( 6 ODIN]
Output: well-trained target feature extractor 6 target classifier Oc: Model Initialization.
I: while not converged do
2: for do=1:N
3:
Sample mini-bate from from {(x), 3/ 12 and {x} 1
4: Compute gradient with cross-entropy classification loss, update @ 0%:
Domain Alignment:
6:
Update @D4, f 6o+} with Eq and Eq. respectivvly to align the domain distribution;
7:
Domain Disentangle:
8:
update eG, @ 6%, 155.,6 90 with Eq.
update Q and {@a.) with Eq.
10:
Mutual Information Minimization:
11:
Calculate mutual information between the disentangled feature pair (fais fae) with M,; Update θ Ar: by 8
12:
13:
end for
Dynamic weight:
15:
Calculate dynamic weight by Eq.
16:
Update @ @c by aggregated [ , 1ON),1N00 ,0,,,,,OON) respectively with the computed dynamic weight;
17:
end while
return Oc,, C
manner, which means we cannot train a single model which has access to the source domain and target domain simultaneously. To address this issue, we propose federated adversarial alignment that divides optimization into two independent steps, a domain-specifee local feature extractor and a global discriminator. Specifically, (1) for each domain, we train a local feature extractor, G, for D; and G, for D., (2) for each (D,. D,) source-target domain pair, train adversarial domain identifier DI to align the distributions in an adversarial manner: we first train DI to identify which domain are the features come from, then we train the generator (G;, G₃) to confuse the DI. Note that D only gets access to the output vectors of G, and G,, without violating the UFDA setting. Given the i-th source domain data X target data X", the objective for DI, is defined as follows:
in
Ladvpi, @
X 8: XJ Gr, Gr) = -Exiixxs [log DI(GG((**)) Ex [lo([1 - DI,(G((x)))]
(4)
In the second step, Luvv remains unchanged, but Lavv is updated with the following objective:
Trddg g0, eG
S x DI,) = Ex Dog DT,(G.(x**)) Ex [log (x,()(
(5)
Representation Disentanglement We employ adversarial disentanglement to extract the domain-
invariant features. The high-level intuition is disentangle the features extracted by (G,, G.) into domain-invariant and domain-specific features. As shown in Figure [(b]] the disentangler D;
separates the extracted features into two branches. Specifically, we first train the K-way classifier C,
and K K-way class identifier CI,, to correctly predict the labels with a cross-entropy loss, based on fas and fas features, respectively. The objective is:
K 1k= y (og((
Treross-entrop = - -e yD)a "ir,eer,eCiiee
Σ 1 y Bee
(6) DG, denote the domain- domain-invarian invariant and domain-specite
where fa = (xx(())))
features respectively. In the next step, we freeze the class identifier CI, and only train the feature disentangler to confuse the class identifier CI, by generating the domain- aomain--pecific features fas. as shown in Figure This can be achieved by minimizing the negative entropy loss of the predicted
5

---

Published as a conference paper at ICLR 2020
3
5
7
8
60 conete denrr and fieee
pirt inf
5
7
8
Ielp quaa te mom
3
8 8 8
03
5
La Se
rd
up
0
3 S
7
(a) Digit-Five
leckgack mositer kendplose
(b) OfliceCallteh10
like
airplaze
(c) DumainNet
ball
loade lerd ureniarr
(d) Amazon Review
Figure 2: We demonstrate the effectiveness of FADA on four datasets: (1) "Digit-Five" which includes MNIST (ma)), MNIST-M (mm)), SVHN (sv), Synthetic (syn), and USPS (up). (2) Office-Calteche Caltech1O dataset, which contains Amazon (A), Caltech (C), DSLR (D), and Webcam (W). (3) DomainNet dataset, which includes: clipart (clp), infograph (inf) ). painting (prt), quickdraw (qdr), real (rel), and sktech (skt). (4) Amazon Review dataset, which contains review for Books (B), DVDs (D), Electronics (E), and Kitchen housewares (K).
class distribution. The objective as follows:
No,
1 N. N.. Σ log (T((D)(G(((*))
Leent @%, ,e.
No. Σ log CI(())))
Feature disentanglement facilitates the knowledge transfer by reserving Sa and dispelling sas. To enhance the disentanglement we minimize the mutual information between domain- innariiiinaviantt features and domain- domain-specifee specific features, following Peng et al. 2019).. Specifically, the mutual information is defined I(fai: fas) Spx o log dPrepe dIP Q where PpQ is the joint probability distribution of (fas fas), and Pp- = So dd Po = So dPpp are the marginals. Despite being a pivotal measure across different distributions, the mutual information is only tractable for discrete vari- ables, for a limited family of problems where the probability distributions are unknown (Belghazi) et al., 2018). Following Peng et al. (201)), we adopt the Mutual Information Neural Estimator (MINE) (Belghazi al. 2018) to estimate the mutual information by leveraging neural network
dl're
To: I(P; 2), = supace Epp..) [To] - log(E pf spn (e" Practically, MINE can be calculated as I(P; Q) s o (p. q) T(p, q. 0) log(ʃ] J PP (p) (g)ee(n))))) To avoid computing the integrals, we leverage Monte-Carlo integration to calculate the estimation:
I(P, ()= = Σ T(p.9.0) - log( 1 Σ
T(p 00)
(8)
where (p. q) are sampled from the joint distribution, g' is sampled from the marginal distribution, and T(p, q, ()) is the neural network parameteralized by estimate the mutual information between P and Q, we refer the reader to MINE (Belghazi et al., 2018) for more details. The domain-invariin and domain- specific features are forwarded a reconstructor with a L2 loss to reconstruct the original features, aming to keep the representation integrity, as shown in Figure I(b)
Optimization Our model is trained in an end-to-end fashion. We train federated alignment and representation disentanglement component with Stochastic Gradient Descent (Kiefer et al. 1952). The federated adversarial alignment loss and representation disentanglement loss are minimized together with the task loss. The detailed training procedure is presented in Algorithm
5 EXPERIMENTS
We test our model on the following tasks: digit classification (Digit-Five), object recognition (Office- CaltechI0 (Gong et al. 2012). DomainNer (Peng al.) (0188) and sentiment analysis (Amazon Review dataset (Blitzer et al.) 2007a)). Figure Saaurre@shhon shows some data samples and Table D (see supplementary material) shows the number of data per domain we used in our experiments. We perform our experiments on 10 Titan-Xp GPU cluster and simulate the federated system on single machine (as the data communication not the main focus of this paper). Our model is implemented with Py Torch. We repeat every experiment 10 times on the Digit- FFve and Amazon Review datasets, and 5 times on the Office-Ceer Caltech10 and DomainNer (Peng et al. 2018) datasets, reporting the mean and standard derivation of accuracy. To better explore the effectiveness of different components of our model, propose three different ablations, í.e. model with dynamic attention; model II: I + adversarial alignment; and model III: Il representation disentanglement

---

Published as conference paper at ICLR 2020
Models
mt.mm..yy.up
Avg
Source Only
63.3+10.7
90.5-10.8
88.7+0.8
63.5-10.9
82.44:0.6
77.7
DAN
63.7+0.7
96.3+0.5
94.2=0.8
62.420.7
85.410.7
80.4
DANN
71.3+0.55
97.610.77
92.310.8
63.420.7
85.340.8
82.1
Source Only
49.6
75.4
22.7+0.9
44.3:10.7
75.5+1.4
53.5
AdaBN
59.3+0.8
75.3+007
34.2=0.6
59.7=0.7
87.1
61.3
AutoDIAL
60.7#1.6
76.8+0.9
32.4=0.5
58.7=1.2
90.3+109
65.8
S-DANN
59.5+0.6
86.111.1
44.3
53.4110.9
89.7+0.9
66.6
F-DAN
$7.5+00.8
86.4
45.3+0.7
58.4-00.7
40.81.11
67.7
(I)
44.2+t0.7
90.5+0.8
27.8+0.5
55.6=0088
88.3+1.2
61.3
tadeerarvaaa (II)
58,2
9.9
48,310.6
62.1=0.5
90.6
70.3
FADA FADA+disentangle (III)
62.5+0.7
91.4 +0.7
50.5 +003
71.8+005
91.7++1.0
73.6
Table I: Accuracy (%) on "Digit-Five dataset with UFDA protocol. FADA achieves 73.6%, outperforming other baselines. We incrementally add each component t our model, aiming to study their effectiveness on the final results. (model I: with dynamic attention; model II: Itadversarial alignment: model III: -- disentanglement. mt, up, sv, sy, abbreviations for MNIST, USPS, SVHN, Synthetic Digits, MNIST-M.)
-10-
-2
-21
-3
2
-30
Source Features
EDANN Features
(c) f-DAN Features
(d) Features
Figure 3: Feature visualization: t-SNE plot of source-only features, f-DANN (Ganin & Lempitsky 2015)
features, f-DAN (Long et al., 2015)) features and FADA features in sv sv-mm.mt mt.sy up setting. We use different markers and colors denote different domains. The data points from target domain have been denoted by red for better visual effect. (Best viewed in color.)
5.1 EXPERIMENTS ON DIGIT RECOGNITION
Digit-Fiv Five This dataset a collection of five henchmarks for digit recognition, namely MNIST (Le Cun et al.) 1998). Synthetic Digits (Ganin & Lempitsky, 2015), MNIST-M (Ganin & Lempitsky 2015), SVHN, and USPS. In our experiments, we take turns setting one domain as the target domain and the rest as the distributed source domains, leading to five transfer tasks. The detailed architecture of our model can be found Table
(see supplementary material).
Since many DA models (Saito al. 2018 French al. 2018 Hoffman et al. 2018 require access to data from different domains, it is infeasible to directly compare our model to these baselines. Instead, compare our model the following popular domain adaptation baselines: Domain Adversarial Neural Network (DANN) (Ganin & Lempitsky 2013). Deep Adaptation Network (DAN) (Long et al., 2015), Automatic Domaln Alignment Layers (AutoDIAL) (Carlucci et al., 2017). and Adaptive Batch Normalization (A (AdaBN) Li et al. 2016). Specifically, DANN minimizes the domain gap between source domain and target domain with gradient reversal layer. DAN applies multi-kernel MMD loss (retton et al.) 2007) to align the source domain with the target domain in Reproducing Kernel Hilbert Space. AutoDIAL introduces domain aligmment layer to deep models match the source and target feature distributions to reference one. AdaBN applies Batch Normalization layer (Toffe Szegedy 2015) to facilitate the knowledge transfer between the source and target domains. When conducting the baseline experiments, modify the original settings to fit federated DA setting (i.e. each domain has its own model), denoted by f-DAN and f-DANN. In addition, demonstrate the difficulty of UFDA where accessing all source data with a single model is prohibative, we also perform the corresponding multi- DA experiments (shared source data).
the code provided by the authors and
Results and Analysis The experimental results are shown in Table From the results, we can make the following observations. (1) Model III achieves 73.6% average accuracy, significantly

---

Published as a conference paper at ICLR 2020
Method
C.D.W A
A,D.W -
A,C,W DD
A,C.D W
Average
AlexNet
80.1=0.4
86.9
82.7=0.5
85.1
83.7
f-DAN
82.5
87.2#0.4
85.6#0.4
86.1+0.3
85,4
f-DANN
83.120.4
86.5
84.820.5
86.420.5
85.2
FADA +attention (I)
81.2+00.3
87.140.6
83.5+0.5
85.9+0.4
84.4
FADA +aDA+aaversarial (II)
83.1+0.6
87.8+0.4
85.4+0.4
86.8+0.5
85.8
FADA (III)
84.3+0.6
88.4
86.1=0.4
87.3+0.5
86.5
ResNet101
81.9
87.9+0.33
85.7=0.5
86,9
85.6
AdaBN
82.2:0.4
88.2+0.6
85.920.7
87.4
85.7
AutoDIAL
83.3+:0.6
87.7+0.88
85.6
87.1
85.9
S-DAN
82.7+00.3
88.1+0.5
86.5+
86.5++0.3
85.9
f-DANN
83.5=0.4
88.5
85.9
87.1=0.4
86.3
aattntntnntinn (I)
82.1
87.5+0.33
85.8=0.4
87.3+0.55
85.7
aadesaaveraariil (II)
83.2++0.4
88.4+0.3
86.4+0.5
87.8+0044
86.5
FADA +disentangle (III)
84.2++0.
88.7+
87.1+0.6
88.1+004
87.1
Table 2: Accuracy on Office-Caltechl Caltech10 dataset with unsupervised federated domain adaptation protocol. The upper table shows the results for AlexNet backbone and the table below shows the results for ResNet backbone.
2.0
0.5
0.4
1.0
Oynamic
0.2
0.1
0.0
50
100
150
0.0
Besting
kum of traiing
(a) A. Disistance
(b) Triining loss for A.C.W-D
(c) DAD confusion matrix
(d) FADA confusion
Figure 4: A-istaaee of ResNet, J-DANN, and FADA features on two different tasks. (b) training errors and dynamic weight on W task. (c)-(d) confusion matrices of f-DAN, and FADA on A,C task.
outperforming the baselines. (2) The results of model I and model II demonstrate the effectiveness of dynamic attention and adversarial alignment. (3) Federated DA displays much weaker results than multi-source DA, demonstrating that the newly proposed UFDA learning setting is very challenging.
To dive deeper into the feature representation of model versus other baselines, plot in Figure B(a) 3(d) the t-SNE embeddings of the feature representations learned on mmm.mtsv.sy-uup task with source-only features, f-DANN features, f-DAN features and FADA features, respectively. We observe that the feature embeddings of our model have smaller intra-class variance and larger inter-class variance than f f-DANN and f-DAN, demonstrating that our model capable of generating the desired feature embedding and can extract domain-invarianl features across different domains.
5.2 EXPERIMENTS ON OFFICE-CALTEENE
Offic-Calltech1 Goog et al. 2012) This dataset contains 10 common categories shared by Office- 31 (Saenko al. 2010) and Caltech-225 datasets (Griffin et al. (007). contains four domains: Caltech (C), which are sampled from Caltech-256 dataset, Amazon (A), which contains images collected from amazon. com| Webcam (W) and DSLR (D), which contains images taken by web camera and DSLR camera under office environment.
leverage two popular networks as the backbone of feature generator G, i.e. AlexNet (rrizhessyy et al. 2012) and ResNet (He et al.) 2016). Both the networks are pre-trained on ImageNet (Deng et al. 2009). Other components of our model are randomly initialized with the normal distribution. In the learning process,
the learning rate of randomly initialized parameters to ten times of the pre-trained parameters as will take more time for those parameters to converge. Details of our model are listed in Table (supplementary material).
8

---

Published as a conference paper at ICLR 2020
Models
pifpni, gult rel,akt-<clp
rel,skt-innf
clp.pns.qdr
clp.
rel,skt pplskk-ppnt
elp.ird rel, sel,skt--qur
clp, pn..nf.pnt. qdrsst-orel
elpanef
qulsret
Avg
AlexNet
39.2=2077
12.7=0.4
32.7=0.4
5.9=0.7
40.340.5
22.7=0.6
25.6
f-DAN
41.6:1.66
13.7=0.5
36.3:0.5
6.5=0..5
43.5+0.
22.9=
27.4
f-DANN
42.6=
14.1=0.7
35.2:10.3
6.2.t0.7
42.9:10.5
22.7=00.7
27.2
FADA +.b wnttngle (I)
44.9++0.7
15.9++0.6
36.3+-0.8
8.6++0.8
44.5++0.6
23.2++0.8
28.9
ResNet101
41.6
14.5=1.7
35.7=0.7
8.4:0.7
43.5+...7
23.3:10.7
27.7
fDAAN
43.5++0.7
14.1+0.6
37.6+00.7
8.3+-066
44.5+00.5
25.1+0.5
28.9
FDAANN
43.1+-0.8
15.2+00.9
35.7++0.4
8.2+0.66
45.2++077
27.1+0.6
29.1
FADA Adisenaanne (II)
45.3++0.7
16.3+0.8
38.9 +0.7
7.9+0.4
46.7+00.4
26.8+00.4
30.3
Table 3: Accuracy (%) the DomainNet dataset (Peng al.
2018
dataset under UFDA protocol. The upper
table shows the results based on AlexNet (Krizhevsky al. 2012)
backbone and the table below are the results
based on ResNet (He al. 2016) backbone.
Method Source Only
D,E,K +
B,E,K
B,D.K +
B,D,E + K
Average
74.4
79.2+00.4
73.5
71.4.0.1
74.6
f-DANN
75.2++0.
82.7+0.2
76.5+0.3
72.8+0.4
76.8
AdaBN
76.7+10.3
80.911.3
75.7+0.2
74.6110.3
76.9
AutoDIAL
76.3:10.4
81.3.0..5
74.8+00.4
75.6+10.2
77.1
f-DAN
75.6=10.2
81.6+0.3
77.9
73.2=0.2
77.6
FADA+attentin (I)
74.8=10.2
78.9
74.5+0.3
72.5+0.2
75.2
FADA +adversarial (II)
79.7+-0.2
81.1+0.11
77.3+0.2
76.4--0.2
78.6
FADA FADA (I
78.1+0022
82.7++0.1
77.4+0.2
77.5++0.3
78.9
Table Accuracy (%) on "Amazon Review" dataset with unsupervised federated domain adaptation protocol.
Results and Analysis The experimental results on Office-Caltech 10 datasets are shown in Table 2 We utilize the same backbones the baselines and separately show the results. We make the following observations from the results: (1) Our model achieves 86.5% accuracy with an AlexNet backbone and 87.1% accuracy with ResNet backbone, outperforming the compared baselines. (2) All the models have similar performance when C.D.W are selected as the target domain, but perform worse when A is selected as the target domain. This phenomenon probably caused by the large domain gap, the images in A are collected from amazon com and contain white background.
To better analyze the effectiveness of FADA, we perform the following empirical analysis: (1) A- distance Ben-David et al. (2010) suggests A-distance as measure of domain discrepancy, Following Long al. 20155), we calculate the approximate A-distance d 2 21 2e) for C.D.W TD,W-+J and A,C.W-9 >D tasks, where e the generalization error of a two-sample classifier (e.g. kernel SVM) trained the binary problem of distinguishing input samples between the source and target domains. In Figure 4(a) we plot for tasks with raw ResNet features, fDANNN features, and FADA features, respectively. We observe that the d on DADA features are smaller than ResNet features and f- DANN features, demonstrating that FADA features harder to be distinguished between source and target. (2) To show how the dynamic attention mechanism benefits the training process, we plot the training loss w/ or w/o dynamic weights for A.C,W-D task in Figure 4bb The figure shows the target model's training error is much smaller when dynamic attention is applied, which is consistent with the quantitative results.
addition, in A, A,C. WD setting, the weight of A decreases to the lower bound after first few epochs and the weight of W increases during the training process, as photos both and W are taken in the same environment with different cameras. (3) To better analyze the error mode, we plot the confusion matrices for f-DAN and FADA on A.C.D->W task in Figure (4d) The figures show that f-DAN mainly confuses "calculator" vs. "keyboard", "backpack" with "headphones" while FADA is able distinguish them with disentangled features.
5.3 EXPERIMENTS ON DOMAINNET
DomainNet This dataset contains approximately 0.6 million images distributed among 345 cate- gories. It comprises of six domains: Clipart (clp), a collection of clipart images; Infograph (inf),
http:://a bu. /M33IA/

---

Published as a conference paper at ICLR 2020
target
md
3V
19
up
Avg
C
Avg
E
Avg
FADA w/o, atlention
60.1
91.2
49.2
69.1
90.2
71.9
83.3
85.7
86.2
88.3
85.8
77.2 82.8
77.2
76.3
78.3
FADA attention
62.5
91.4
50.5
71.8
91.7
73.6
84.2
88.7
87.1
88.1
87.1
78.1 1 82.7
77.4
77.5
78.9
Table 5: The ablation study results show that the dynamic attention module is essential for our model.
infographic images with specific object; Painting (pnt), artistic depictions of object in the form of paintings; Quickdraw (qdr), drawings from the worldwide players of game "Quick Draw! Real photos and real world images; and Sketch (skt), sketches of specific objects. This dataset is very large-scale and contains rich and informative vision cues across different domains, providing good testbed for unsupervised federated domain adaptation. Some sample images can be found in Figure 2
Results The experimental results on DomainNet are shown in Table 3 Our model achieves 28.9% and 30.3% accuracy with AlexNet and ResNet backbone, respectively. In both scenarios, our model outperforms the baselines, demonstrating the effectiveness of our model on large-scale dataset. Note that this dataset contains about 0.6 million images, and even one-percent performance improvement not trivial. From the experiment results, we can observe that all the models deliver less desirable performance when infograph and quickdraw are selected the target domains. This phenomenon is mainly caused by the large domain shift between inflqdr domain and other domains.
5.4 EXPERIMENTS ON AMAZON REVIEW
Amazon Review (Blitzer et al. 2007a) This dataset provides a testbed for cross-domain sentimental analysis of text. The task is to identify whether the sentiment of the reviews is positive or negative. The dataset contains reviews from jamzoon com users for four popular merchandise categories: Books (B), DVDs (D), Electronics (E), and Kitchen appliances (K). Following Gong et al. (2013), we utilize 400-dimensional bag-of-worte representation and leverage a fully connected deep neural network as the backbone. The detailed architecture of our model can be found in Table g (supplementary materials).
Results The experimental results on Amazon Review dataset are shown in Table Our model achieves accuracy of 78.9% and outperforms the compared baselines. We make two major observations from the results: (1) Our model is not only effective on vision tasks but also performs well linguistic tasks under UFDA learning schema. (2) From the results of model and II, can observe the dynamic attention and federated adversarial alignment are beneficial to improve the performance. However, the performance boost from Model II to Model III is limited. This phenomenon shows that the linguistic features are harder to disentangle comparing to visual features.
5.5 ABLATION STUDY
To demonstrate the effectiveness of dynamic attention, perform the ablation study analysis. The sshlss the results on "Digit-Five", Office-Caltechh and Amazon Review benchmark. We observe that the performance drops in most of the experiments when dynamic attention model is not applied. The dynamic attention model is devised to cope with the varying convergence rates in the federated learning system, i.e., different source domains have their convergence rate. In addition, it will increase the weight of specific domain when the domain shift between that domain and the target domain is small, and decrease the weight otherwise.
CONCLUSION
In this paper, we first proposed novel unsupervised federated domain adaptation (UFDA) problem and derived a theoretical generalization bound for UFDA. Inspired by the theoretical results, we proposed a novel model called Federated Adversarial Domain Adaptation (FADA) to transfer the knowledge learned from distributed source domains to an unlabeled target domain with a novel dynamic attention schema. Empirically, we showed that feature disentanglement boosts the perfor- mance of FADA in UFDA tasks. An extensive empirical evaluation on UFDA vision and linguistic benchmarks demonstrated the efficacy of FADA against several domain adaptation baselines.
https://quickkee wwwp:w/wwwwhhgggggeooooooooooo com/dat.
10

---

Published as a conference paper at ICLR 2020
REFERENCES
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm. Mutual information neural estimation. In Jennifer Dy and An- dreas Krause (eds.), Proceedings of the 35th International Conference volume 80 of Proceedings of Machine Learning Research, pp. 531-540, Stockholmsmässan, Stockholm Sweden, 10-15 Jul 2018. PMLR. [ 1880bellhhae 8a.html
Machine Learning,
http: httt::///proceedings...tt mlr. press/
Shai Ben-David John Blitzer, Koby Crammer, Fernando Pereira, et al. Analysis of representations for domain adaptation. Advances in neural information processing systems, pp. 137-144, 2007.
Shai Ben-Benniiiieee John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. A theory of learning from different domains. Machine learning, 79(1-2):111-175, 2010.
Yoshua Bengio, Aaron Courville, and Pascal Vincent. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence. 3798)11798-1828, 2013.
Blitzer, M. Dredze, and F. Percira. Biographics, bollywood. boom-boxes and blenders: Domain adaptation for sentiment classification. Proceedings of the 45th annual meeting of the association of computational linguistics, pp. 440-447, 2007a.
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman. Learning bounds for domain adaptation. In Proc. NIPS, 2007b.
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman. Learning bounds for domain adaptation. In J. C. Platt, D. Koller, Y. Singer, and S. T. Roweis (eds.), Advances in Neural Information Processing Systems 20, pp. 129-136. Curran Associates, Inc., 2008. URL fittp://papers nips. caass.ccpppprrrr 3212-leas -o- formmma i n adaptationnn...m on. pdf
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. Practical secure aggregation for privacy- preserving machine learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175-1191, ACM, 2017.
Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, and Samuel Rota Bulò. Autodial: Automatic domain alignment layers. In International Conference on Computer Vision, 2017.
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In Computer Vision and Puttern Recognition, 2009. CVPR 2009.
IEEE Conference on, pp. 248-254-2 255.255. IEEE, 2009.
Geoff French, Michal Mackiewicz, and Mark Fisher. Self-ensembling for visual domain adaptation. In International Conference Learning Representations, 2018. URL U hwtp::ttttp/neeeeeeeeeee net / Eorum? 2aeiummiiii-ppee I.xoTxxx
Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backpropagation. In Francis Bach and David Blei (eds.), Proceedings of the 32nd International Conference on Ma-
chine Learning, volume 37 of Proceedings of Machine Learning Research, pp. 1180-1189, Lille, France, 07-09 Jul 2015. PMLR. URL http:tt::pp/cceeeeeeeee ganin15. html]
ml www.sppresss/ce. v
Muhammad Ghifary, W Bastiaan Kleijn, and Mengjie Zhang. Domain adaptive neural networks for object recognition. In Pacific Rim international conference on artificial intelligence, pp. 994-904. Springer, 2014.
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, David Balduzzi, and Wen Li. Deep reconstruction-caasiffeee networks for unsupervised domain adaptation. In European Confer- ence on Computer Vision pp. 597-613. Springer, 2016.
11

---

Published as a conference paper at ICLR 2020
Ran Salad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Nachrig, and John Wernsing. Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. In International Conference Machine Learning, pp- 201-210, 2016.
Boqing Gong, Yuan Shi, Fei Sha, and Kristen Grauman. Geodesic flow kernel for unsupervised domain adaptation. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 1066-2073. IEEE, 2012.
Boqing Gong, Kristen Grauman, and Fei Sha. Connecting the dots with landmarks: Discrimina- tively learning domain-invariant features for unsupervised domain adaptation. In International Conference Machine Learning, pp. 222-230, 2013.
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances neural informa- tion processing systems. pp. 2672-2680, 2014.
Arthur Gretton, Karsten M Borgwardt, Malte Rasch, Bernhard Schölkopf, and Alex Smola. A kernel method for the wwo-sample-probles In Advances neural information processing systems, pp. 513-520, 2007.
Gregory Griffin, Alex Holub, and Pietro Perona. Caltech-256 object category dataset. 2007.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.
Judy Hoffman, Eric Tzeng, Taesung Park, Jun Zhu, Phillip Isola, Kate Saenko, Alexei Efros,, and Trevor Darrell. CyCADA: Cycle-consistent adversarial domain adaptation. In Jennifer Dy and Andreas Krause (eds.), Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pp. 1989- 1998, Stockholmsmässan. Stockholm Sweden, 10-15 Jul 2018. PMLR. URL httpt:t:pppoocceedrnrtoooo mww.mnr..mmm.m....m.mm.mm.rm..mm mlr press/ v800/of fman 8a html
Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pp. 448-456, 2015.
Jack Kiefer, Jacob Wolfowitz, et Stochastic estimation of the maximum of a regression function. The Annals of Mathematical Statistics, 23(3):462-466, 1952.
Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, and Jiwon Kim. Learning to discover cross-domain relations with generative adversarial networks. In Doina Precup and Yee Whye Teh (eds.), Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pp. 1857-1865, International Convention Centre, Sydney, Australia, 06-11 Aug 2017. PMLR. URL htt.t:::/ppocceelnggs.ttt pre.ss 270/kim174 html
Diederik Kingma and Max Welling. arXiv:1312.6114 2013.
Auto-encoding variational bayes.
arXiv preprint
Durk P Kingma, Shakir Mohamed, Danilo Jimenez. Rezende, and Max Welling, Semi-supervised learning with deep generative models. In Advances in neural information processing systems, pp. 3581-3589, 2014.
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolu- tional neural networks. In Advances in neural information processing systems, pp. 1097-1105. 2012.
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEFF, 36(11):2278-224 1998.
12

---

Published as a conference paper at ICLR 2020
Hsin- Ying
Hsin-Ying Lee, Hung- Yu Tseng, Jia-Bin Huang, Maneesh Singh, and Ming-Hsuan Yang. Diverse image-to-image translation via disentangled representations. In Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, and Yair Weiss (eds.). Computer Vision - ECCV 2018, pp. 36-52, Cham, 2018. Springer International Publishing. ISBN 978-3-030-01246-55
Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, and Xiaodi Hou. Revisiting batch normalization for practical domain adaptation. arXiv preprint arXiv: 100.0079, 2016.
Alexander H. Liu, Ven-Cheng Liu, Yu- Yu-Ying Ying Yeh, and Yu-Chiang Frank Wang. A unified feature disentangler for multi-domain image translation and manipulation. CoRR, 0bs//889.0133/ 2018a. URL httphhtpp///xiii/
wwgg/bb............. /tobs//88... 01361
Ming- Yu Liu and Oncel Tuzel. Coupled generative adversarial networks. In Advances in neural information processing systems, pp. 469 469-477, 2016.
Yang Liu, Tianjian Chen, and Qiang Yang. Secure federated transfer learning. arXiv preprint arXiv:1812.03332 2018b.
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. Learning transferable features with deep adaptation networks. In Francis Bach and David Blei (eds.), Proceedings of the 32nd International Conference on Machine Learning, volume of Proceedings of Machine Learning Research, pp. 105, Lille, France, 07-09 Jul 2015. PMLR. URL http:://proceeed.n/// mlr.press/v3l hwwpppres//v37/oog1..ttttt http///lnn11...mm.mmm
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I. Jordan. Deep transfer learning with joint adaptation networks. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, pp. 2208-2217, 2017. URL press /or html
http://proceeding
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael Jordan. Conditional adversarial domain adaptation. In Advances in Neural Information Processing Systems, pp. 1640-1650, 2018.
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey. Adversarial autoencoders. ICLR workshop, 2016.
Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh, and A R. Domain adaptation with multiple sources. In D. Koller, D. Schuurmans, Y. Bengio, and L., Bottou (eds.), Advances in Neural Information Processing Systems 21, pp. 1041-1048. Curran Associates, Inc., 2009.
Michael F Mathieu, Junbo Jake Zhao, Junbo Zhao, Aditya Ramesh, Pablo Sprechmann, and Yann LeCun. Disentangling factors of variation in deep representation using adversarial training. In Advances in Neural Information Processing Systems. pp. 5040-5048, 2016.
Payman Mohassel and Peter Rindal. Aby mixed protocol framework for machine learning. In Proceedings of the 2018 ACM SIGSAC Conference Computer and Communications Security, pp. 35-52. ACM, 2018
Payman Mohassel and Yupeng Zhang. Secureml: A system for scalable privacy-preserte machine learning. In 2017 IEEE Symposium on Security and Privacy (SP), pp. 19-38. IEEE, 2017.
Augustus Odena, Christopher Olah, and Jonathon Shlens. Conditional image synthesis with auxiliary classifier GANs. In Doina Precup and Yee Whye Teh (eds.), Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pp. International Convention Centre, Sydney, Australia, 06-11 Aug 2017. PMLR. URL htttp://proceee:: www.://weeei...mmm.......m.m.mm wtwp::pppssss..oo.. ooeeaa///naa///ao hhml
Sinno Jialin Pan and Qiang Yang. A survey on transfer learning. IEEE Transactions on knowledge and data engineering. 13155))134551335 2010.
Xingchao Peng and Kate Saenko. Synthetic to real adaptation with generative correlation alignment networks. In 2018 IEEE Winter Conference Applications of Computer Vision, WACV 2018, Lake Tahoe, NV, USA, March 12-15, 2018, pp. 1982- 1991, 2018, doi: 10.1109/WAC22218 00219. URL htt.:httt///tttc / .rgg 10. 11 09/ WACV. 2018. 2018.00219
13

---

Published as a conference paper at ICLR 2020
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang. Moment matching for multi-source domain adaptation. arXiv preprint
2018.
Xingchao Peng, Zijun Huang, Ximeng Sun, and Kate Saenko. Domain agnostic learning with disentangled representations. ICML, 2019.
Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence. Dataset Shift in Machine Learning. The MIT Press, 2009. ISBN 0262170051, 9780262170055.
Ronald L Rivest, Len Adleman, Michael L Dertouzos, al. On data banks and privacy homomor- phisms. Foundations of secure computation, 4(11):169-180, 1978.
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell. Adapting visual category models to new domains. In European conference on computer vision, pp. 213-226. Springer, 2010.
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada. Maximum classifier discrepancy for unsupervised domain adaptation. In The IEEE Conference on Computer Vision and Puttern Recognition (CVPR), June 2018.
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar. Federated multi-task learning. In Advances in Neural Information Processing Systems, pp. 4424 4434, 2017.
Baochen Sun and Kate Saenko. Deep CORAL: conrelation alignment for deep domain adaptation. CoRR, abs/ RBV1100001119 2016.
http://v.rrg/ab////ooooa/ 011.711111 719
Robert Tibshirani, Guenther Walther, and Trevor Hastie. Estimating the number of clusters in data set via the gap statistic. Journal of the Royal Statistical Sociery: Series (Statistical Methodology), 63(2):411-42) 2001.
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474. 2014.
Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko. Simultaneous deep transfer across domains and tasks. In The IEEE International Conference on Computer Vision (ICCV), December 2015.
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. Adversarial discriminative domain adaptation. Computer Vision and Puttern Recognition (CVPR), volume pp. 4, 2017.
Vapnik and Vlamimir Vapnik. Statistical learning theory wiley, New York, pp. 156-160, 1998.
Zili Yi, Hao (Richard) Zhang, Ping Tan, and Minglun Gong. Dualgan: Unsupervised dual learning for image-to-image translation. In ICCV, pp. 2868-2888 2876, 2017.
Han Zhao. Shanghang Zhang, Guanhang Wu, José MF Moura, Joao P Costeira, and Geoffrey J
Gordon. Adversarial multiple source domain adaptation. In Advances in Neural Information Processing Systems, pp. 8559-8570, 2018.
Yun Zhu, Taesung Park, Phillip Isola, and Alexei Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Computer Vision (ICCV), 2017 IEEE International Conference on, 2017.
14

---

Published as a conference paper at ICLR 2020
7 NOTATIONS
We provide the explanations of notations occurred in this paper.
Notations
Name
Details
DI
domain identifier
align the domain pair GG disentangle the feature to fas and feature fds- predict the labels for input features extract domain-specific features. extract features for Ds. extract features for D,
D
disentangler
C
classifier
CT
class identify
G
feature generator
G
feature generator
M
mutual information estimator
estimate the mutual information between fas and fa
Table 6: Notations occurred in the paper.
8
MODEL ARCHITECTURE
We provide the detailed model architecture and Table 9 for each component in our model: Generator, Disentangler, Domain Classifier, Classifier and MINE.
layer configuration
Feature Generator
Conv2D (3, 64, 5, I, 2), BN, ReLU, MaxPool
2 Conv2D (64, 64, 5, 1, 2), BN, ReLU, MaxPool
3
Conv2D (64, 128, 5, 1, 2), BN, ReLU
Disentangler FC (8192, 3072), BN, ReLU DropOut (0.5), FC (3072, 2048), BN, ReLU Domain Identifier
2
FC (2048, 256), LeakyReL.U
FC (256, 2), LeakyReI.U
Class Identifier
FC (2048, 10), BN, Softmax
Reconstructor FC (4096, 8192)
Mutual Information Estimator
fel
FC (2048, 512), LeakyReLU
fel_y
(2048, 512), LeakyRel.U
2
FC (512,1)
Table 7: Model architecture for digit recognition task ("Digit-Five" dataset). For each convolution layer, we list the input dimension, output dimension, kernel size, stride, and padding. For the fully- connected layer, provide the input and output dimensions. For drop-out layers, we provide the probability of an element to be zeroed.
DETAILS OF DATASETS
We provide the detailed information of datasets. For Digit-Five and DomainNet, we provide the train/test split for each domain. For OfficeCClttcchl provide the number of images in each domain. For Amazon review dataset, we show the detailed number of positive reviews and negative reviews for each merchandise category.
15

---

Published as a conference paper at ICLR 2020
layer
configuration
Feature Generator
FC (400, 128), BN, ReLU
Disentangler
FC (128, 64), BN, ReLU
2
DropOut (0.5), FC (64, 32), BN, ReLU
Domain Identifier
FC (64, 32), LeakyReI.U
FC (32, 2), LeakyReLU
Class Identifier
FC (32, 2). BN, Softmax
Reconstructor
FC (64, 128)
Mutual Information Estimator
fcl_x
FC (32, 16), LeakyReI.U
fcl_y
FC (32, 16), LeakyReI.U
2
FC (16.1)
Table 8: Model architecture for cross-doman sentimental analysis task ("Amazon Review" dataset (Blitzer et al., (2007a]) For the fully-connected layers (FC), we provide the input and output dimensions. For drop-out layers (Dropout), provide the probability of an element to be zeroed.
layer
configuration
Feature Generator:
ResNet101 or AlexNet
Disentangler
(0.5. FC (2048, 2048), BN, ReLU
2
(O.5 FC (2048, 2048), BN, ReLU
Domain Identifier
FC (2048, 256), LeakyReLU
2
FC (256, 2), LeakyReLU
Class Identifier
FC (2048, 10), BN, Softmax
Reconstructor FC (4096, 2048) Mutual Information Estimator
fel_x
FC (2048, 512), LeakyReLU
fcl_y
FC (2048, 512), LeakyReLU
FC (512,1)
Table 9: Model architecture for image recognition task (Office- (Office-Caltedl) Caltech10 (Gong et al. 2012) and DomainNet (Peng al. (2188)) For each convolution layer, we list the input dimension, output dimension, kernel size, stride, and padding. For the fully-connected layer, we provide the input and output dimensions. For drop-out layers, provide the probability of an element to be zeroed.
16

---

Published as a conference paper at ICLR 2020
Digit-Five
Splits Train
mnist
mnist m
svhn
syn
usps
Total
25,000
25,000
25,000
25,000
7,348
107,348
Test
9,000
9,000
9,000
9,000
1,860
37,860
Office Caltech00
Splits Total
Amazon 958
Caltech
Dstr
Webcam
Total 2,533
1,123
157
295
DomainNet
Splits
clp
inf
pnf
qdr
rel
skt
Total
Train
34,019 14,818
37,087
52,867
120,750
122,563
49,115 21,271
416,401
Test
16,114
22,892
51,750
52,764
179.609
Amazon Review
Splits
Books
DVDs
Electronics
Kitchen
Total
Positive
1,000
1,000
1,000
1,000
4,000
Negative
1,000
1,000
1,000
1,000
4,000
Table 10: Detailed number of samples we used in our experiments.
17

---

Published as a conference paper at ICLR 2020
10 PROOF OF THEOREM 2
Theorem 3. (Weighted error bound for federated domain adapiation). Let H be a hypothesis class with VC-dimension d and {Da,},' 1 Drr be empirical distributions induced by a sample of size m from each source domain and target domain in a federated learning system, respectively. Then, Wa IR
aq = with probability at least 8 over the choice of samples, for each H.
Σ [ (dnan (D. + +4V
2d loglo((NN)) (2Nm) + log(4/6)
en(hr) Eg( E aghs,) +
Nm
iE[N]
iE[N]
(D.,.Dr) divergonce
VC-Dimension Constraint
201Or OR Source
(9) where d is the risk of the optimal hypothesis on the mixture of Ds, and T, and $ is the mixture of source samples with size Nm.
Proof. Consider combined source domain which is equivalent to a mixture distribution of the N source domains, with the mixture weight o, where a E T and >i 0;; = 1. Denote the mixture source domain distribution as D3 (where D3 := EiE(N] a,Ds,), and the data sampled from D3 as assume D3 and Dr be the source domain and target domain, respectively,
S. Theoretically, Apply Theorem [] we have that for 0 < 5 < 1, with probability of at least 1-5 over the choice of samples, for each h H.
1 (D Dr) + +4V 2
2d 2d + (4/()))) + Xa
cr (h) s (g) +
(10)
Nm where Ao the risk of optimal hypothesis on the S and T. The upper bound of dHAH (Ds, Dr) can be derived as follows:
jHAN (Dg, = sup (Pr(A) Pr(A)]
AEAnaR D3
Dr
2 sup Σ
0i Pr (A)
Pr(AA)]
AEAHAR iE[M]
Dz.
DT
sup Σ AEAHaN KE[N]
aal Pr (A) Pr(A))]
Dg.
Dr
2 Σ as sup AEAnAH
(( (A) - Pr((A))
Ds; Dr
<E[N]
andHAn (Ds,, Dr)
EEIN]
the first inequality is derived by the triangle inequality. Similarly, with the triangle inequality property, we can derive Aa < ExE(M agA₃, On the other hand, for Vhr E H. we have: (g(hr) =
Eg(E ashs.). Replace (h),, Aa and duan (Ds. Dr) in Eq. 10 we have:
er(hr) S (hrr)) + 1 duan(Os. Dr) +4
2d 2d + log(4/0)
+ Aa
2
Nm
E Σ
ashs,) ) + dHAH (Dg, DT) + +4/ 2d + log(4/0)
1
+ Aa
2
Nm
2d (og(2m) + log(4/8)
< ( Σ aghs,) ) +
Σ
aNCHAN (Ds, Dr) + Σ
ond; + 41
2
Nm
ME[M]
NE[N]
iE[M]
Eg( ayhs.)
( dHa Dr) +hl) + +V
Σ
as
2d 2d + log 4/0)
Nm
SE[N]
(Ds, divergence
VC-Dimension Constraint
erfor on source
18

---

Published as a conference paper at ICLR 2020
Remark. The equation in Theorem 2 provides theoretical error bound for unsupervised federated domain adaptation as it assumes that the source data distributed on different nodes can form a mixture source domain. In fact, the data on different node not be shared under the federated learning schema. The theoretical error bound is only valid when the weights of models on all the nodes are fully synchronized.
19


Reviews:
[{"review_id": "r1xvdmZJ5B", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The authors present a novel algorithm for dealing with domain adaptation in the setting of federated learning (classification, specifically). That is, they tackle the issue of learning a model on a new domain when access to the data points used in training the source models is not possible due to privacy constraints. The approach uses the gradients of the source models, reweighed to account for the differing shifts between the different sources and the target domain, to fit the model on the target domain.\n\nThe authors motivate their approach by providing a novel bound on the generalization error of transfer learning when the hypothesis function used on the target domain is a convex combination of hypotheses fitted on multiple source domains. This bound shows that the weighted sum of divergences in the symmetric difference hypothesis space controls the generalization error, so the authors aim at deriving feature representations and using aggregation weights that ensure this weighted sum is small. \n\nThe authors use a novel dynamic attention model to get the aggregation weights: they cluster the features in the target domain, and measure how much the intra-cluster variation decreases when information from a given source domain is incorporated. The aggregation weights for the model updates on the target domain are then weighed using a softmax transform of these contribution weights. \n \nThe motivation up through and including section 3 is clear, the theoretical results are presented clearly, but the model details in section 4 are unclear:\n-  In the dynamic attention mechanism, how does one a priori choose the number of clusters in computing the gap statistics, and what is the impact? \n- the notation in the federated adversarial alignment section is unclear: what *exactly* are the model coefficients Theta that are being updated?\n- the statement \"optimize following objective\" is made several times. this is ambiguous, and should be corrected to \"miminize\" following objective.\n- the representation disentanglement process is intricate, and only vaguely addressed. how does one fit the neural net and use (8)? where is the l2 reconstruction loss balanced with the mutual information? the vagueness of this section means Algorithm 1 is not well-specified.\n\nThe experiments are reasonable, and compare to baseline domain adaptation methods.\n\nThe problem considered is of interest, and the approach is novel and interesting. However, the algorithm is not described in sufficient detail. After reading the paper, and spending considerable time rereading section 4, I still do not understand how Algorithm 1 is implemented in practice. For that reason I lean towards reject. I will update my score if the authors clarify the details of Algorithm 1. \n\nComments:\n- the symmetric difference hypothesis space is incorrectly called the HdeltaH divergence in section 3\n", "extra_scores": {"experience_assessment": "I do not know much about this area.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}, {"review_id": "SJgnuHj6KB", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The paper proposed and studied the unsupervised federated domain adaption problem, which aims to transfer knowledge from source nodes to a new node with different data distribution. To address the problem, a federated adversarial domain adaption (FADA) algorithm is introduced in the paper. The key idea of the algorithm is to update the target model by aggregating the gradients from source nodes, and also leverage adversarial adaption techniques to reduce the discrepancy between source features and target features. Overall, the problem studied in the paper is interesting, theoretical analysis on the error bound is provided in the paper, and the effectiveness of the proposed method has been validated in various datasets. Although the technical contributions of the paper are solid, I still have several concerns about it.\n1. The proposed algorithm is not described very clearly in section 4. According to the paper, DI is used to identify the domain from the output of Gi and Gt and align the features from those domains, then how is it related to the disentanglement in Eq 6. Also in Eq 6, symbol C_s was not introduced in the previous context, which makes it confusing to understand this objective.\n\n2. It would be better if the author(s) can provide some complexity analysis of the proposed algorithm.\n\n3. The paper still contains some typos and unresolved reference issues. ", "extra_scores": {"experience_assessment": "I have read many papers in this area.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}, {"review_id": "BJe8hCFWqB", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper introduces an unsupervised federated domain adaptation (UFDA) problem and proposes a new model called Federated Adversarial Domain Adaptation (FADA) to transfer the knowledge learned from distributed source domains to an unlabeled target domain. This paper uses a dynamic attention mechanism by leveraging the gap statistics to transfer distributed source knowledge. This paper also proposes a method to disentangle the domain-invariant features from domain-specific features, using adversarial training. Moreover, a theoretical generalization bound for UFDA is derived. An extensive empirical evaluation is performed on UFDA vision and linguistic benchmarks.\n\nThis paper should be rejected because the total pipeline seems ad-hoc except for optimizing the weight of the source domain in the attention mechanism. Although the derivation of generalization bound for FDA in Sec.3 is excellent, it only demonstrates the importance of the weight $\\alpha$. This result is trivial if we assume to have the same source domain as the target and utterly unrelated source domain to the target domain. It seems that proving why minimizing the gap statistics contributes to FADA is more essential in the dynamic attention mechanism. Because representation disentanglement has no relation with the derived theory, it would be better to clarify whether this method is original or not.\n\nIn the UFDA setting, the reviewer has doubts about whether it is realistic that the source node has a rich labeled data assuming our smartphones. Also, the assumption that the system cannot access the source data but must access all source feature seems a significant limitation in terms of privacy issues and communication cost between the target node and the source nodes.\n\nIt is unclear what is the final target classifier. If the target can access the teaching signal (e.g., labels or tags) in the source domains, it would be better to mention whether this situation violates the assumption the authors raised or not.\n\nMinor comments\n1) What is T(p, q, \\theta) in the section of Representation Disentanglement?\n\n2) What is C_s in eq.6? C_{s_i}?\n\n3) In Fig.3, it is not proper to discuss the size of intra-class variance by just looking at the figures because the t-SNE is a non-linear mapping. It is better to show quantitative scores, such as the value of the Fisher criterion.", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper studies an interesting new problem, federated domain adaptation, and proposes an approach based on dynamic attention, federated adversarial alignment, and representation disentanglement.

Reviewers generally agree that the paper contributes a novel approach to an interesting problem with theoretical guarantees and empirical justification. While many professional concerns were raised by the reviewers, the authors managed to perform an effective rebuttal with a major revision, which addressed the concerns convincingly. AC believes that the updated version is acceptable.

Hence I recommend acceptance.
Decision: Accept (Poster)