=Paper=
{{Paper
|id=Vol-3177/paper2
|storemode=property
|title=Addressing Privacy in Recommender Systems with Federated Learning
|pdfUrl=https://ceur-ws.org/Vol-3177/paper2.pdf
|volume=Vol-3177
|authors=Vito Walter Anelli,Tommaso Di Noia,Eugenio Di Sciascio,Antonio Ferrara,Alberto Carlo Maria Mancino
|dblpUrl=https://dblp.org/rec/conf/iir/AnelliNSFM22
}}
==Addressing Privacy in Recommender Systems with Federated Learning==
https://ceur-ws.org/Vol-3177/paper2.pdf
Addressing Privacy in Recommender Systems with
Federated Learning
Discussion Paper
Vito Walter Anelli1 , Tommaso Di Noia1 , Eugenio Di Sciascio1 , Antonio Ferrara1,∗ and
Alberto Carlo Maria Mancino1,∗
1
Politecnico di Bari, Bari, Italy
Abstract
In recent years, recommender systems have successfully assisted user decision-making in various user-
centered applications. In such scenarios, the modern approaches are based on collecting user-sensitive
preferences. However, data collection is crucial since users now worry about the related privacy risks
when sharing their data. This work presents a recommendation approach based on the Federated
Learning paradigm, a distributed privacy-preserving approach to the recommendation. Here, users
collaborate on the training while still controlling the amount of the shared sensitive data. This paper
presents FPL: a pair-wise learning-to-rank approach based on Federated Learning. We show that it puts
users in control of their data and reveals recommendation performance competing with centralized
state-of-the-art approaches. The public implementation is available at https://split.to/sisinflab-fpl.
Keywords
Federated Learning, Collaborative Filtering, Pair-wise Learning, Matrix Factorization
1. Introduction
Recommender Systems (RSs) have emerged as a solution for mitigating the information-overload
problem by assisting users with personalized recommendations. Generally, these models
are trained in a centralized fashion, where massive proprietary sensitive data are hosted on
a single server. In the last two decades, the RS mainstream research line has focused on
Collaborative Filtering (CF) [2, 3], Content-based [4, 5], and hybrid [6] approaches. However,
these models need sufficient data to provide accurate recommendations by exploiting users’
behavior similarities. Proposed by Google, Federated Learning (FL) emerged as a privacy-by-
design solution for machine learning models [7, 8, 9, 10]. FL addresses the ML-privacy limitations
by horizontally distributing the training while having the clients train the global model on their
local devices without sharing their private data [10]. Recent works on Federated Learning-based
RSs have exhibited benefits for the users’ privacy [11]. Federated Pair-wise Learning (FPL) [8]
shows how a federated recommender system can exploit the Learning-to-Rank competitive
performance while still letting users control their data. Indeed, one of the most significant
IIR2022: 12th Italian Information Retrieval Workshop, June 29 - June 30th, 2022, Milan, Italy
Extended version [1] published in the 58th volume of Journal of Intelligent Information Systems
∗
Corresponding author.
Envelope-Open antonio.ferrara@poliba.it (A. Ferrara); alberto.mancino@poliba.it (A. C. M. Mancino)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
advantages of employing FPL is that the users participating in the federated learning process can
independently decide how much they are inclined to disclose their private sensitive preferences.
In this paper, we formally show how in FPL, the users can control their data. We investigate
the risks related to the transmission of the gradients and how we address this drawback by
putting users in control of their data. Moreover, we show that integrating FPL could lead to
competitive performance in accuracy and provide users with a trustworthy model [12].
2. Foundations of Federated Pair-wise Learning
FPL: Federated Pair-wise Learning for Recommendation. Let be 𝒰 and ℐ the set of users
and items, respectively, and X ∈ ℝ|𝒰|×|ℐ | be the user-item matrix containing for each 𝑥𝑢𝑖 an
implicit feedback 1 or 0. Inspired by the state-of-the-art MF [13, 14, 15, 16], each user 𝑢 and item
𝑖 are represented by the embedding vectors p𝑢 and q𝑖 , respectively. The dot product between p𝑢
and q𝑖 can explain any observed user-item interaction 𝑥𝑢𝑖 , so that any non-observed interaction
can be estimated as 𝑥̂𝑢𝑖 = 𝑏𝑖 + p𝑇𝑢 q𝑖 , where 𝑏𝑖 is a bias term. FPL builds a global model Θ𝑆 = ⟨Q, b⟩
on a server 𝑆, which is aware of the whole catalog ℐ, and a private local model Θ𝑢 = ⟨p𝑢 ⟩ on
each client of the federation. In our federated setting, each user 𝑢 holds her own feedback
dataset x𝑢 ∈ ℝℐ , which — compared with a centralized recommender system — corresponds to
the 𝑢-th row of matrix X, so only user 𝑢 knows her own set of consumed items.
In FPL, the model is trained by rounds of communications composed of a four-step protocol
(Distribution→Computation→Transmission→Aggregation), described in the following.
1. Distribution. 𝑆 delivers the model Θ𝑆 to a subset of selected users 𝒰 − ⊆ 𝒰.
2. Computation. Each user 𝑢 generates 𝑇 triples (𝑢, 𝑖, 𝑗) from her local dataset and for each
of them performs BPR stochastic optimization to compute the updates for the local p𝑢
vector of Θ𝑢 , and for p𝑖 , 𝑏𝑖 , p𝑗 , and 𝑏𝑗 of the received Θ𝑆 , following:
⎧(q𝑖 − q𝑗 ) if 𝜃 = p𝑢 ,
⎪p𝑢 if 𝜃 = q𝑖 ,
𝑒 −𝑥̂𝑢𝑖𝑗 𝜕 𝜕 ⎪
Δ𝜃 = ⋅ 𝑥̂ − 𝜆𝜃, with 𝑥̂𝑢𝑖𝑗 = −p𝑢 if 𝜃 = q𝑗 , (1)
1+𝑒 −𝑥̂𝑢𝑖𝑗 𝜕𝜃 𝑢𝑖𝑗 𝜕𝜃 ⎨
⎪1 if 𝜃 = 𝑏𝑖 ,
⎪
⎩−1 if 𝜃 = 𝑏𝑗 .
3. Transmission. Each client 𝑢 ∈ 𝒰 − send back the updates for the computed item
embedding and item bias to the server 𝑆. We should focus on how BPR computes the
training output. Since for a triple (𝑢, 𝑖, 𝑗), the server could be able to distinguish the
consumed item 𝑖 from the non-consumed one 𝑗 (for instance, by analyzing the positive
and the negative sign of Δ𝑏𝑖 and Δ𝑏𝑗 ), we argue that sending all the updates computed
by 𝑢 may raise a privacy issue. FPL proposes a solution to overcome this vulnerability
by sending the sole update (Δq𝑗 , Δ𝑏𝑗 ) of each training triples (𝑢, 𝑖, 𝑗). In this way, the
user 𝑢 shares only indistinguishably negative or missing values, which are assumed to
be non-sensitive. Furthermore, FPL allows users to establish the number of consumed
items to share with the central server 𝑆, by introducing the parameter 𝜋. It is related to
the probability of a user sending a specific update relative to a positive item (Δq𝑖 , Δ𝑏𝑖 ) in
addition to (Δq𝑗 , Δ𝑏𝑗 ).
4. Global aggregation. All the received updates are aggregate by 𝑆 in Q and b to build the
new model Θ𝑆 ← Θ𝑆 + 𝛼 ∑𝑢∈𝒰 − ΔΘ𝑢 , with 𝛼 being the learning rate.
Privacy Analysis of FPL. FPL has not been conceived as a privacy-preserving framework but
as a tool to control the trade-off between potentially exposed sensitive data and the recommen-
dation quality. While federated learning hides, by design, users’ raw data to the server, some
malicious actors might still try to learn sensitive information. For this reason, federated learning
alone does not consider providing privacy guarantees to users. In the context of FPL, the aim
is to protect the existence of user-item transactions. While attempts of active reconstruction
of the user profile are not considered here and are out of the scope of this work, we focus
on the presence of an honest-but-curious server. Regarding Eq. 1, suppose a pair of positive
and negative items 𝑖 and 𝑗 and the gradients received at the 𝑡-th round of communication. The
notation of Δq𝑡𝑖 and Δq𝑡𝑗 could be extended by focusing on a single latent factor 𝑓:
Δq𝑡𝑖,𝑓 = p𝑡−1 𝑡−1 𝑡−1 𝑡−1
𝑢,𝑓 𝜎 (p𝑢,𝑓 (q𝑖,𝑓 − q𝑗,𝑓 )), (2)
Δq𝑡𝑗,𝑓 = −p𝑡−1 𝑡−1 𝑡−1 𝑡−1
𝑢,𝑓 𝜎 (p𝑢,𝑓 (q𝑖,𝑓 − q𝑗,𝑓 )), (3)
where 𝜎(⋅) returns values in the range (0, 1). These equations show that the modules of Δq𝑡𝑖,𝑓 and
Δq𝑡𝑗,𝑓 (which must be sent to the server) are identical, while their signs are opposite. Moreover,
the sign of the update depends on both the existence/absence of a transaction for 𝑘 and on
𝑠𝑔𝑛(p𝑡−1
𝑢,𝑓 ). Therefore, the sign of a gradient does not directly reveal the presence or absence of
an item in the user’s training set. However, the pairs of positive and negative gradients disclose
user preference patterns. In a round of communication, all the updates for the consumed items
share the same sign, as well as all the updates for the non-consumed items have the same
positive or negative sign, depending on 𝑠𝑔𝑛(p𝑡−1 𝑢,𝑓 ). If the server 𝑆 is honest-but-curious (i.e., it
may try to inspect the updates to obtain some user information), as soon as it obtains enough
information adequate to identify one or more consumed/non-consumed items, the entire user
dataset will be exposed. To avoid this problem, FPL puts users in control of their data. If the
users adopt the privacy-oriented masking procedure during the Transmission phase, they can
decide the fraction of updates for positive items to send. In the case of exposure of the user
transactions, only a fraction is given up. As a consequence, FPL has to work in a data scarcity
scenario, where the fraction of used data is defined by the parameter 𝜋 (this could be fixed by the
system designer or actively decided by the users). In the experimental section, we empirically
show how FPL is resilient to missing data in the federated scenario.
3. Experimental Results
In the following, we report the accuracy performance of FPL. It has been evaluated on the
Foursquare dataset [17] in the Point-of-Interest domain since it contains data usually perceived
as sensible. To mimic a federation of devices in a single country, we have extracted check-ins
Table 1
Results of accuracy metrics for baselines and FPL on the three datasets. The metrics refers to the top 10
recommendation list.
Brazil Canada Italy
P@10 R@10 P@10 R@10 P@10 R@10
Random 0.00013 0.00015 0.00030 0.00035 0.00030 0.00029
Centralized
Top-Pop 0.01909 0.02375 0.04239 0.04679 0.04634 0.05506
User-kNN 0.10600 0.13480 0.07639 0.07533 0.06881 0.07833
Item-kNN 0.07716 0.09607 0.04006 0.03881 0.04663 0.05356
VAE 0.10320 0.13153 0.06060 0.06317 0.10421 0.21324
BPR-MF 0.07702 0.09494 0.03694 0.03650 0.04560 0.05458
FCF 0.03089 0.03749 0.03724 0.03836 0.03126 0.03708
Federated
sFPL * 0.07757 0.09581 0.04515 0.04550 0.04701 0.05600
sFPL+ * 0.08682 0.11004 0.05701 0.05665 0.05595 0.06229
pFPL * 0.07771 0.09582 0.04582 0.04637 0.04642 0.05465
pFPL+ * 0.08733 0.11085 0.05761 0.05755 0.05565 0.06291
* Best 𝜋 obtained for each the proposed FPL variations across three countries (Brazil, Canada,
and Italy) are: sFPL = (0.5, 0.1, 0.4), sFPL+ = (0.9, 0.4, 0.2), pFPL = (0.8, 0.1, 1), pFPL+ = (0.8, 0.3, 0.1)
for three countries, namely Brazil, Canada, and Italy. Moreover, we have split each dataset by
adopting a realistic temporal hold-out 80-20 splitting on a per-user basis [18, 19]. FPL has been
evaluated with different values of 𝜋 in [0.0, 1.0] with step 0.1, in order to assess the impact of
user sharing more (high 𝜋) or less (low 𝜋) positive feedbacks. Hence, four configurations have
been considered regarding variations in computation ad communication. In sFPL and pFPL,
the model is updated for each round of communication involving one client, or all the clients,
respectively. In these configurations, the clients’ local training involves only one triple (𝑢, 𝑖, 𝑗)
from their local dataset. In contrast, in the correspondent sFPL+ and pFPL+ configurations, the
number of selected triples is proportional to the number of each user’s positive feedback. FPL
has been compared against six centralized models (Random, Most Popular, BPR-MF [20],
User-kNN and Item-kNN [21], VAE [22]), and a federated recommendation approach based on
MF (FCF [23]). The accuracy performances of the results are reported in table 1, comparing the
four configurations of FPL and the state-of-the-art baselines. We notice that VAE and User-kNN
outperform the other models, while Item-kNN and BPR-MF show similar results. Regarding
FPL, when comparing sFPL and sFPL+ with their parallelized configurations (pFPL and pFPL+),
we observe that the increased parallelism does not affect the performance significantly. On
the other hand, increasing the local computation (sFPL+ and pFPL+) boots the performance up
to 24%. The results show that FPL behaves better than BPR-MF in precision and recall. These
performances are surprising considering that FPL exploits less feedback per round since they
are reduced by 𝜋. It is also notable that FPL outperforms FCF and preserves privacy to a greater
extent since sharing gradients of all rated items in FCF can result in a data leak [24].
We have seen how the proposed system can generate recommendations with a quality that
is comparable with the centralized pair-wise learning approach. Moreover, the increased local
computation causes a considerable improvement in the accuracy of recommendations. On the
other side, the training parallelism does not significantly affects results. Finally, when the local
computation is combined with parallelism, the results show a further improvement.
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