Federated Linear Contextual Bandits
Year: 2021

Abstract:
This paper presents a novel federated linear contextual bandits model, where individual clients face different K-armed stochastic bandits coupled through common global parameters. By leveraging the geometric structure of the linear rewards, a collaborative algorithm called Fed-PE is proposed to cope with the heterogeneity across clients without exchanging local feature vectors or raw data. Fed-PE relies on a novel multi-client G-optimal design, and achieves near-optimal regrets for both disjoint and shared parameter cases with logarithmic communication costs. In addition, a new concept called collinearly-dependent policies is introduced, based on which a tight minimax regret lower bound for the disjoint parameter case is derived. Experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real-world datasets.

heading: 1 Introduction
text: Federated learning (FL) (McMahan et al., 2017) is an emerging distributed machine learning (ML) paradigm where massive number of clients collaboratively learn a shared prediction model while keeping all the training data on local devices. Compared with standard centralized machine learning, FL has the following characteristics (Kairouz et al., 2021):
• Heterogeneous local datasets. The local datasets, which are often generated at edge devices, are likely drawn from non-independent and identically distributed (non-IID) distributions.
• Communication efficiency. The communication cost scales with the number of clients, which is one of the primary bottlenecks of FL. It is critical to minimize the communication cost while maintaining the learning accuracy.
• Privacy. FL protects local data privacy by only sharing model updates instead of the raw data.
While the main focus of the state-of-the-art FL is on the supervised learning setting, recently, a few researchers begin to extend FL to the multi-armed bandits (MAB) framework (Lai and Robbins, 1985; Auer et al., 2002; Bubeck and Cesa-Bianchi, 2012; Agrawal and Goyal, 2012, 2013a). In the canonical setting of MAB, a player chooses to play one arm from a set of arms at each time slot. An arm, if played, will offer a reward that is drawn from its distribution which is unknown to the player. With all previous observations, the player needs to decide which arm to pull each time in order to maximize the cumulative reward. MAB thus represents an online learning model that naturally captures the intrinsic exploration-exploitation tradeoff in many sequential decision-making problems.
Extending FL to the MAB framework is naturally motivated by a corpus of applications, such as recommender systems, clinical trials, and cognitive radio. In those applications, the sequential decision making involves multiple clients and is distributed by nature. While classical MAB models assume immediate access to the sequentially generated data at the learning agent, under the new
35th Conference on Neural Information Processing Systems (NeurIPS 2021).
realm of FL, local datasets can be stored and analyzed at the clients, thus reducing the communication load and potentially protecting the data privacy.
Despite the potential benefits of FL, the sequential decision making and bandit feedback bring new challenges to the design of FL algorithms in the MAB setting. Different from the supervised learning setting where static datasets are collected beforehand, under the MAB setting, data is generated sequentially as decisions are made, actions are taken, and observations are collected. In order to maximize the cumulative reward and minimize the corresponding learning regret, it thus requires sophisticated coordination of the actions of the clients. The heterogeneous reward distributions across clients make the coordination process even more convoluted and challenging. Besides, the data privacy and communication efficiency requirements result in significant challenges for efficient information exchange and aggregation between local clients and the central server.
In this work, we attempt to address those challenges in a federated linear contextual bandits framework. This particular problem is motivated by the following exemplary applications.
• Personalized content recommendation. For content (arm) recommendation in web-services, user engagement (reward) depends on the profile of a user (context). The central server may deploy a recommender system on each user’ local device (client) in order to personalize recommendations without knowing the personal profile or behavior of the user.
• Personalized online education. In order to maximize students performances (reward) in online learning, the education platform (central server) needs to personalize teaching methods (arms) based on the characteristics of individual students (context). With the online learning software installed at local devices (client), it is desirable to personalize the learning experiences without allowing the platform to access students’ characteristics or scores.
In those examples, the reward of pulling the same arm at different clients follows different distributions dependent on the context as in contextual bandits (Auer, 2003; Langford and Zhang, 2008). We note that conventional contextual bandits is defined with respect to a single player, where the time-varying context can be interpreted as different incoming user profiles. In contrast, we consider a multi-client model, where each client is associated with a fixed user profile. The variation of contexts is captured over clients as opposed to over time. Although the set of clients remains fixed through the learning process, the reward of pulling the same arm still varies across clients. Such a model naturally takes data heterogeneity into consideration. Besides, we adopt a linear reward model, which has been widely studied in contextual bandits (Li et al., 2010; Agrawal and Goyal, 2013b).
Main contributions. Our main contributions are summarized as follows.
First, we propose a new federated linear contextual bandits model that takes the diverse user preferences and data heterogeneity into consideration. Such a model naturally bridges local stochastic bandits with linear contextual bandits, and is well poised to capture the tradeoffs between communication efficiency and learning performances in the federated bandits setting.
Second, we design a novel algorithm named Fed-PE and further develop its variants to solve the federated linear contextual bandits problem. Under Fed-PE, clients only upload their local estimates of the global parameters without sharing their local feature vectors or raw observations. It not only keeps the personal information private, but also reduces the upload cost. We explicitly show that Fed-PE and its variants achieve near-optimal regret performances for both disjoint and shared parameter cases with logarithmic communication costs.
Third, we generalize the G-optimal design from the single-player setting (Lattimore and Szepesvári, 2020) to the multi-client setting. We develop a block coordinate ascent algorithm to solve the generalized G-optimal design efficiently with convergence guarantees. Such a multi-client G-optimal design plays a vital role in Fed-PE, and may find broad applications in related multi-agent setups.
Finally, we introduce a novel concept called collinearly-dependent policy and show that the celebrated LinUCB type of policies (Li et al., 2010), Thompson sampling based policies with Gaussian priors (Agrawal and Goyal, 2013b), and least squared estimation based policies, such as Fed-PE, are all in this category. By utilizing the property of collinearly-dependent policies, we are able to characterize a tight minimax regret lower bound in the disjoint parameter setting. We believe that this concept may be of independent interest for the study of bandits with linear rewards.
√
Notations. Throughout this paper, we use ‖x‖V to denote √ xᵀV x. The range of a matrixA, denoted by range(A), is the subspace spanned by the column vectors of A. We use A† and Det(A) to denote the pseudo-inverse and pseudo-determinant of square matrix A, respectively. The specific definitions can be found in Appendix B of the supplementary material.

heading: 2 Related Works
text: Collaborative and distributed bandits. Our model is closely related to the collaborative and distributed bandits when action collision is not considered. Landgren et al. (2016, 2018) and Martínez-Rubio et al. (2019) study distributed bandits in which multiple agents face the same MAB instance, and the agents collaboratively share their estimates over a fixed communication graph in order to design consensus-based distributed estimation algorithms to estimate the mean of rewards at each arm. Szorenyi et al. (2013) considers a similar setup where in each round an agent is able to communicate with a few random peers. Korda et al. (2016) considers the case where clients in different unknown clusters face independent bandit problems, and every agent can communicate with only one other agent per round. The communication and coordination among the clients in those works are fundamentally different from our work.
Wang et al. (2020) investigates communication-efficient distributed linear bandits, where the agents can communicate with a server by sending and receiving packets. It proposes two algorithms, namely, DELB and DisLinUCB, for fixed and time-varying action sets, respectively. The fixed action set setting is similar to our setup, except that it assumes that all agents face the same bandits model, which does not take data heterogeneity into consideration.
Federated bandits. A few recent works have touched upon the concept of federated bandits. With heterogeneous reward distributions at local clients, Shi and Shen (2021) and Shi et al. (2021) investigate efficient client-server communication and coordination protocols for federated MAB without and with personalization, respectively. Agarwal et al. (2020) studies regression-based contextual bandits as an example of the federated residual learning framework, where the reward of a client depends on both a global model and a local model. Li et al. (2020) and Zhu et al. (2021) focus on differential privacy based local data privacy protection in federated bandits. While the linear contextual bandit model considered in Dubey and Pentland (2020) is similar to this work, it focuses on federated differential privacy and proposes a LinUCB-based FedUCB algorithm, which incurs a higher regret compared with our result for the shared parameter case. A regret and communication cost comparison between Fed-PE and other baseline algorithms is provided in Table 1.

heading: 3 Problem Formulation
text: Clients and local bandits model. We consider a federated linear contextual bandits setting where there are M clients pulling the same set of K items (arms) denoted as [K] := {1, 2, . . . ,K}. At each time t, each client i ∈ [M ] pulls an arm ai,t ∈ [K] based on locally available information. The incurred reward yi,t is given by yi,t = ri,ai,t + ηi,t, where ηi,t is a random noise, and ri,ai,t is
1Results adapted from Dubey and Pentland (2020) by letting the privacy budget 1/ go to 0. 2Results adapted from the single-player linear contextual bandits studied in Dimakopoulou et al. (2017) by
assuming instantaneous information exchange and sequential decision-making at the central server.
the unknown expected reward by pulling arm ai,t. We note that without additional assumptions or interaction among the clients, each local model is a standard single-player stochastic MAB, where classic algorithms such as UCB (Auer and Ortner, 2010) and Thompson sampling (Agrawal and Goyal, 2012) are known to achieve order-optimal regret.
Linear reward structure with global parameters. In order to capture the inherent correlation between rewards of pulling the same arm by different clients, we assume ri,a has a linear structure, i.e., ri,a = x ᵀ i,aθa, where xi,a ∈ Rd is the feature vector associated with client i and arm a, and θa ∈ Rd is a fixed but unknown parameter vector for each a ∈ [K]. Here we use xᵀ to denote the transpose of vector x. The same arm a may have different reward distributions for different clients, due to potentially varying xi,a across clients. Such a linear model naturally captures the heterogeneous data distributions at the clients, yet admits possible collaborations among clients due to the common parameters {θa}a∈[K]. When θa varies for different arm a, it is called the disjoint parameter case; when θa is known to be a constant across the arms, it is the shared parameter case. We investigate both cases in Sections 4 and 5, respectively.
Communication model. We assume there exists a central server in the system, and similar to FL, the clients can communicate with the server periodically with zero latency. Specifically, the clients can send “local model updates” to the central server, which then aggregates and broadcasts the updated “global model” to the clients. (We will specify these components later.) Note that just as in FL, communication is one of the major bottlenecks and the algorithm has to be conscious about its usage. Similar to Wang et al. (2020), we define the communication cost of an algorithm as the number of scalars (integers or real numbers) communicated between server and clients. We also make the assumption that clients and server are fully synchronized (McMahan et al., 2017).
Data privacy concerns. Similar to Dubey and Pentland (2020), our contextual bandit problem involves two sets of information that are desirable to be kept private to client i: the feature vectors {xi,a}a∈[K] and the observed rewards {yi,t}t∈[T ]. Different from the differential privacy mechanism adopted in Dubey and Pentland (2020), in this work, we aim to communicate estimated global model parameters {θa}a between the clients and the server. This is consistent with the FL framework, where only model updates are communicated instead of the raw data.
Assumption 1 We make the following assumptions throughout the paper:
1) Bounded parameters: For any i ∈ [M ], a ∈ [K], we have ‖θa‖2 ≤ s, 0 < ` ≤ ‖xi,a‖2 ≤ L. 2) Independent 1-subgaussian noise: ηi,t is a 1-subgaussian noise parameter sampled indepen-
dently at each time for each client with E[ηi,t] = 0, E[exp(ληi,t)] ≤ exp(λ 2
2 ) for any λ > 0.
Assumption 1.1 is a standard assumption in the bandit literature, which ensures that the maximum regret at any step is bounded. We emphasize that our work does not make any assumption on the knowledge of suboptimality gaps, nor do we assume the existence of a unique optimal arm at each client.
Our objective is to minimize the expected cumulative regret among all clients, defined as:
E[R(T )] = E [ M∑ i=1 T∑ t=1 ( xᵀi,a∗i θa∗i − x ᵀ i,ai,t θai,t )] , (1)
where a∗i ∈ [K] is an optimal arm for client i: ∀b 6= a∗i , x ᵀ i,a∗i θa∗i − x ᵀ i,bθb ≥ 0.

heading: 4 Federated Linear Contextual Bandits: Disjoint Parameter Case

heading: 4.1 Challenges
text: Solving the federated linear contextual bandits model faces several new challenges. The first challenge is due to the constraint that only locally estimated parameters {θa}a∈[K] are uploaded to the central server. While this is not an issue for stochastic MAB where the {θa}a∈[K] are scalars (Shi and Shen, 2021; Shi et al., 2021), this brings significant challenges for the aggregation of the local estimates into a “global model” in our setup. This is because under the linear reward structure, the locally received rewards {yi,t}t∈[T ] only contain the projection of θa along the direction of xi,a, while the
portion of information lying outside range(xi,a) is not captured in {yi,t}t∈[T ]. Thus, by utilizing {yi,t}t∈[T ], the locally estimated θa, denoted as θ̂i,a, cannot provide any information of θa beyond range(xi,a). Since {xi,a}i∈[M ] are different for the same arm a, the locally estimated {θ̂i,a}i∈[M ] are essentially lying in different subspaces. The central server thus needs to take such geometric structure into account when aggregating {θ̂i,a} to construct the global estimate of θa. The geometric structure of the local rewards also brings another challenge for the coordination of actions of local clients. Intuitively, in order to help client i accurately estimate the expected reward by pulling arm a, it suffices to obtain an accurate projection of θa on range(xi,a); any part of θa lying outside this subspace is irrelevant. Therefore, if two clients i and j have xi,a and xj,a orthogonal to each other, exchanging the local estimates θ̂i,a and θ̂j,a does not help the other client improve her own local estimation. On the other hand, if xi,a and xj,a are completely aligned with each other, θ̂i,a and θ̂j,a can be aggregated directly to improve the local estimation accuracy of both. With M possible subspaces spanned by {xi,a}i, it is highly likely that different clients receive different amounts of relevant information (i.e., information lying in range(xi,a)) through information exchange facilitated by the central server. Therefore, in order to reduce the overall regret, it is necessary to coordinate the actions of clients in a sophisticated fashion.
Third, since the exact knowledge of the local feature vectors {xi,a} are kept from the central server, the server may not have an accurate estimation of the uncertainty level of the local estimates at each client, or how much the coordination would help individual clients. This would make efficient and effective coordination even more challenging.
4.2 Federated Phased Elimination (Fed-PE) Algorithm
To address the aforementioned challenges, we propose the Federated Phased Elimination (Fed-PE) algorithm. The Fed-PE algorithm works in phases, where the length of phase p is fp+K. It contains a client side subroutine (Algorithm 1) and a server side subroutine (Algorithm 2). Throughout the paper we use superscript p to indicate phase p barring explicit explanation. We use Api ⊂ [K] to denote the subset of active arms at client i in phase p, Ap := ∪Mi=1A p i , Rpa := {i : a ∈ A p i }, and define T pi,a as the time indices at which client i pulls arm a during the collaborative exploration step in phase p. Then, the algorithm works as follows.
Algorithm 1 Fed-PE : client i Input: T , M , K, α, fp
1: Initialization: Pull each arm a ∈ [K] and receive reward yi,a; θ̂0i,a ← yi,axi,a ‖xi,a‖2 ; Send {θ̂0i,a}a to the server; A0i ← [K]; p← 1. 2: while not reaching the time horizon T do 3: Receive {(θ̂pa, V pa )}a∈Ap−1 from the server. . Arm elimination 4: for a ∈ Ap−1i do
r̂pi,a ← x ᵀ i,aθ̂ p a, u p i,a ← α ‖xi,a‖V pa /`. (2)
5: end for 6: âpi ← arg maxa∈Ap−1i r̂ p i,a, A p i ← { a ∈ Ap−1i | r̂ p i,a + u p i,a ≥ r̂ p i,â p i − up i,â p i } . 7: Send Api to the central server. . Active arm set updating 8: Receive fpi,a for all a ∈ A p i .
9: for a ∈ Api do . Collaborative exploration 10: Pull arm a for fpi,a times and receive rewards {yi,t}t∈T pi,a .
θ̂pi,a ← ( 1
fpi,a ∑ t∈T pi,a yi,t ) xi,a ‖xi,a‖2 . (3)
11: end for 12: Send {θ̂pi,a}a∈Api to the server; Pull â p i until phase length equals f p +K.

heading: 13: p← p+ 1.
text: 14: end while
At the initialization phase, each client i pulls every arm a ∈ [K] once and receives a reward yi,a, based on which it obtains an estimate of the projection of θa. These estimates are sent to the server to construct a preliminary global estimate of θa for each a. The global estimates {θ̂1a}a and the potential matrices {V 1a }a are then broadcast to all clients, after which phase p = 1 begins. Note that after receiving {θ̂0i,a}i,a, the central server will keep a unit vector ēi,a = θ̂ 0 i,a/‖θ̂0i,a‖ for all i ∈ [M ], a ∈ [K]. Since θ̂0i,a is a scaled version of xi,a, ēi,a lies in range(xi,a), and will be utilized to coordinate the arm pulling process (coined as collaborative exploration), as elaborated in Section 4.3.
At the beginning of phase p, after receiving the broadcast {θ̂pa}a and {V pa }a from the server, each client i will utilize the (θ̂pa, V p a ) pair to estimate the expected rewards ri,a and obtain the confidence level according to Eqn. (2) for each a ∈ Ap−1i . Based on the constructed confidence interval, client i then eliminates some arms in Ap−1i and obtains A p i . Next, each client i sends the newly constructed active arm set Api to the server. The server then decides fpi,a, the number of times client i pulling arm a during the collaborative exploration step in phase p for each i ∈ [M ] and a ∈ Api . The specific mechanism to decide f p i,a is elaborated in Section 4.3.
After the collaborative exploration step, client i performs least-square estimation (LSE) for each arm a ∈ Api based on local observations collected in the current phase according to Eqn. (3) and then sends it to the server for global aggregation. Note that although θ̂pi,a lies in range(xi,a), the exact value of xi,a is not revealed to the server, thus preserving the privacy to certain extent.
Algorithm 2 Fed-PE : Central server Input: T , M , K, α, fp
1: Initialization: Receive {θ̂0i,a}i,a; ēi,a ← θ̂0i,a
‖θ̂0i,a‖ for all i ∈ [M ], a ∈ [K]; V 1a ←
(∑ i∈[M ] θ̂0i,a(θ̂ 0 i,a) ᵀ
‖θ̂0i,a‖
)† ,
θ̂1a ← V 1a (∑ i∈[M ] θ̂ 0 i,a ) for all a ∈ [K]; Broadcast {θ̂1a, V 1a }a∈[K]; p← 1.
2: while not reaching the time horizon T do 3: Receive {Api }i∈[M ]; Set A p ← ∪Mi=1Api ; SetR p a ← {i : a ∈ Api }. 4: Solve the multi-client G-optimal design in (6), and obtain solution πp = {πpi,a}i∈[M ],a∈Api . 5: For every client i, send {fpi,a := dπ p i,af
pe}a∈Api . 6: Receive {(a, θ̂pi,a)}a∈Api from each client i. 7: for a ∈ Ap do . Global aggregation
V p+1a ← ∑ i∈Rpa fpi,a θ̂pi,a(θ̂ p i,a) ᵀ ‖θ̂pi,a‖2 † , θ̂p+1a ← V p+1a ∑ i∈Rpa fpi,aθ̂ p i,a  . (4) 8: end for 9: Broadcast {(θ̂p+1a , V p+1a )}a∈Ap to all clients.
10: p← p+ 1. 11: end while

heading: 4.3 Multi-client G-optimal Design
text: In this subsection, we elaborate the core design of Fed-PE, the collaborative exploration step. There are three main design objectives we aim to achieve: 1) As explained in Section 4.1, one of the main challenges in our federated linear contextual bandits setting is that, each client may benefit differently from the information exchange through the central server. To minimize the overall regret, for each arm a ∈ Ap, it is desirable to ensure that after the global aggregation following the collaboration exploration in phase p, the uncertainty in r̂p+1i,a across the clients is balanced. 2) For each client i, in order to eliminate the sub-optimal arms efficiently, it is also important to guarantee that the uncertainty in r̂p+1i,a across the arms in A p i is balanced. 3) Finally, in order to ensure synchronized model updating, we aim to have each client perform the same number of arm pulling in each phase.
Motivated by those objectives, we propose a multi-client G-optimal design to coordinate the exploration of all clients. Specifically, we define πpi : A p i → [0, 1] as a distribution on the active arm set
Api for each i, and denote πp := (π p 1 , . . . , π p M ) as a vector in R
∑ i∈[M] |A
p i |. Let ei,a := xi,a/‖xi,a‖. We note that ei,a equals to either ēi,a or −ēi,a. Then, we define a feasible set Cp ⊂ R ∑ i∈[M] |A p i | as follows:
Cp = πp ∣∣∣∣∣∣ πpi,a ≥ 0,∀i ∈ [M ], a ∈ A p i ,∑ a∈Api πpi,a = 1,∀i ∈ [M ],
rank({πpi,aei,a}i∈Rpa) = rank({ei,a}i∈Rpa),∀a ∈ Ap  . (5) We can verify that Cp is a convex set. The first two conditions ensure that {πpi,a}a form a valid distribution for each client i. We name the last condition as the “rank-preserving” condition. We note that the subspace spanned by the LHS of the rank-preserving condition is always a subset of that spanned by the RHS. Thus, once the rank is preserved, the subspaces spanned by the LHS and the RHS are the same. Thus, this condition ensures that every dimension of θa lying in range({xi,a}i∈Rpa) will be explored under the collaborative exploration. Any violation of the “rank-preserving” condition will lead to information missing along the unexplored dimensions, which shall be prevented in order to reduce the uncertainty level regarding arm a at every client i ∈ Rpa. Then, we formulate the so called multi-client G-optimal design problem as follows:
minimize G(π) = M∑ i=1 max a∈Api eᵀi,a ( ∑ j∈Rpa πpj,aej,ae ᵀ j,a )† ei,a s.t. πp ∈ Cp. (6)
We note that eᵀi,a (∑ j∈Rpa π p j,aej,ae ᵀ j,a )† ei,a can be interpreted as an approximate measure of the uncertainty level along dimension xi,a if the arms are explored locally according to distributions {πpi }i. Thus, the objective function is an approximate measure of the total uncertainties in the least explored arms at each of the clients. By solving (6), the aforementioned three design objectives can be met. We point out that although the server does not known ei,a, the objective function remains the same when ei,a is replaced by ēi,a. Thus, the server can simply use ēi,a to solve (6).
After solving (6) and obtaining {πpi,a}, the server would set {f p i,a := dπ p i,af pe}a∈Api and send it to client i. Note that after taking the ceiling function, ∑ a∈Api fpi,a may be greater than f p. To ensure synchronized updating, each client i would keep pulling the estimated best arm âpi until the phase length equals fp +K.
We note that the multi-client G-optimal design formulated in (6) is related to the G-optimal design for the single-player linear bandits problem discussed in Lattimore and Szepesvári (2020), and the DELB algorithm for the distributed linear bandits in Wang et al. (2020). However, for such cases, the player(s) faces a single bandit problem, thus the objective is to simply obtain a distribution over a so-called core set of arms in order to minimize the maximum uncertainty across the arms. In contrast, due to the multiple clients involved in the federated bandits setting and the heterogeneous reward distributions, we are essentially solving M coupled G-design problems, one associated with each client. Such coupling effect fundamentally changes the nature of the problem, leading to very different characterization of the problem and numerical approaches.
In Appendix C.1 of the supplementary material, we analyze an equivalent problem of the multi-client G-optimal design. We note that such equivalence essentially generalizes the equivalence between the original G-optimal design and D-optimal design in Lattimore and Szepesvári (2020) to the coupled design case. While the original G-design problem can be approximately solved through the FrankWolfe algorithm under an appropriate initialization (Todd, 2016), solving the multi-client G-optimal design problem is numerically non-trivial. In Appendix C.2, we propose a block coordinate ascent algorithm to solve the equivalent problem of (6) efficiently with guaranteed convergence.
4.4 Theoretical Analysis of Fed-PE
We now characterize the performance of the Fed-PE algorithm.
Theorem 1 Under Assumption 1, with probability at least 1−δ, the cumulative regret under Fed-PE scales in O (√ dKMT (log(K(log T )/δ) + min{d, logM}) ) and the communication cost scales
in O(Md2K log T ).
The complete version of Theorem 1 and its proof can be found in Appendix D in the supplementary material. For the communication cost, at each phase p, client i uploads at most K local estimates with dimension d and downloads at most K global estimates and potential matrices with dimension d and d2, respectively. Thus, the upload cost is O(MdK log T ) and the download cost is O(Md2K log T ).
Remark 1 When we set δ = O( √ dK/MT), the overall regret scales in O( √ dKMT log(MKT )),
and the per-client regret scales in O( √ dKT log(MKT )/M). While the minimax lower bound for standard stochastic MAB scales in Ω( √ KT ), the collaborative learning induced by Fed-PE
leads to √ d/M -fold reduction of the per-client regret. We also note that for single-player linear contextual bandits with disjoint parameters, the best known upper bound scales in Õ( √ dKMT ) (Dimakopoulou et al., 2017) over MT arm pulls, which indicates that the regret of Fed-PE is close to the state-of-the-art centralized algorithms at a communication cost in O(log T ).

heading: 4.5 Enhanced Fed-PE
text: The original Fed-PE algorithm requires exponentially increasing fp in order to achieve the regret upper bound in Theorem 1. This is because in each phase p, we only utilize the rewards collected in phase p− 1 to estimate θa. While this simplifies the analysis, the measurements collected in earlier phases cannot be utilized. In order to overcome this limitation, we propose an Enhanced Fed-PE algorithm by leveraging all historical information. Enhanced Fed-PE achieves different tradeoffs between communication cost and regret performance by adjusting fp. The detailed description and analysis of Enhanced Fed-PE for different selection of fp can be found in Appendix E in the supplementary material.

heading: 4.6 Lower Bound
text: To derive a tight lower bound, we focus on a set of representative policies defined as follows.
Definition 1 (Collinearly-dependent policy) Two clients i and j are called collinear if there exist an arm a ∈ [K] and a subset S ⊂ [M ] such that the following conditions are satisfied: 1) xi,a /∈ span({xm,a|m ∈ S}); and 2) xi,a ∈ span({xm,a|m ∈ S} ∪ {xj,a}). For any two clients i and j that are not collinear, if the action of client i is independent of the action of j under a policy π, then, the policy is called a collinearly-dependent policy.
We note that the definition of collinearly-dependent policies is actually quite natural. Intuitively, for two clients that are not collinear, their local observations on any arm a cannot be utilized to improve each other’s knowledge of their own local models. As a result, they should not affect each other’s decision-making process. As shown in the supplementary material, we can verify that the most celebrated ridge regression based LinUCB type of policies (Li et al., 2010), Thompson sampling based polices with Gaussian priors (Agrawal and Goyal, 2013b), and least-square estimation based policies, including Fed-PE, all fall in this category.
Theorem 2 For any collinearly-dependent policy, there exists an instance of the federated linear contextual bandits such that the regret is lower bounded as R(T ) = Ω( √ dKMT ).
Remark 2 Theorem 2 essentially shows that, even if raw data transmission and instantaneous communication are allowed and other collinearly-dependent policies are adopted, we cannot improve the order of the regret summarized in Theorem 1 much, i.e., Fed-PE is order-optimal up to √ log(KMT ).
The proof of Theorem 2 relies on the construction of a special instance of the federated linear contextual bandits where the clients can be divided into d groups. Clients in each group face the same K-armed stochastic bandits model locally, while clients from two distinct groups are not collinear. Analyzing the regret bound in each individual group, we can show that it is lower bounded by Ω( √ KMT/d). Then, by utilizing the property of collinearly-dependent policies, we can show that the overall regret is lower bounded by Ω( √ dKMT ) for this scenario. More discussions on the collinearly-dependent policies and the complete proof of Theorem 2 can be found in Appendix F in the supplementary material.

heading: 5 Federated Linear Contextual Bandits: Shared Parameter Case
text: The Fed-PE algorithm can be slightly modified for the shared parameter case where θa = θ,∀a ∈ [K]. While the client side operation stays the same, the global aggregation step at the server side in (4) can be changed by letting the “potential matrix” V p be ( ∑ a∈[K](V p a ) †)†, and the global estimator θ̂p
be V p (∑ i∈[M ] ∑ a∈Ap−1i fp−1i,a θ̂ p−1 i,a ) . Below, we present the main result for this case and leave the detailed algorithm description and regret analysis in the supplementary material.
Theorem 3 Under Assumption 1, with probability at least 1−δ, the regret of the adapted Fed-PE for the shared parameter case is upper bounded by O (√ dMT (log((log T )/δ) + min{d, logMK}) ) ,
and the communication cost scales in O((KdM + d2M) log T ). Remark 3 By setting δ = O( √
1/MT), we can show that the overall regret scales in O( √ dMT log(MTK)). We note that this bound improves the regret bound for the no differential privacy guarantee case in Dubey and Pentland (2020) by a factor of √
log T , although our settings are slightly different. By assuming all clients face the same linear bandits in this shared parameter setting, the regret in the federated setting over horizon T must be worse than the linear bandits over horizon MT . Since the latter is lowered bounded by Ω( √ dMT ) (Chu et al., 2011), the minimax regret for the shared parameter setting is bounded by Ω( √ dMT ) as well. Thus, the modified Fed-PE is near-optimal for this case.
In terms of the communication cost, the uploading cost stays the same as in the disjoint parameter case, while the broadcast cost is reduced by a factor of K, since only one potential matrix needs to be broadcast for the shared parameter case.

heading: 6 Experiments
text: Experiment results using both synthetic and real-world datasets are reported in this section to evaluate Fed-PE and the proposed enhancement. Additional experimental details and more experimental results can be found in the supplementary material. We consider four different algorithms, namely, Fed-PE, Enhanced Fed-PE, local UCB without communication, and a modified Fed-PE algorithm with full information exchange after collaborative exploration in each phase (coined as ‘Collaborative’ in Figure 1). For all experiments, we set T = 217, fp = 2p, p ∈ {1, 2, . . . , 16}, and run 10 trials. For Fed-PE and its variants, we choose δ = 0.1. Note that other values of δ may further improve the regret. We evaluate the algorithms on both synthetic and MovieLens-100K datasets.
Synthetic Dataset: We first set M = 100,K = 10, and d = 3. We set {θa} as the canonical basis of R3. The feature vectors xi,a are generated randomly ensuring that the suboptimality reward gaps lie in [0.2, 0.4] and ` = 0.5, L = 1. The per-client cumulative regret as a function of T is plotted in Figure 1(a). We see that Enhanced Fed-PE outperforms Fed-PE while being slightly worse than ‘Collaborative’. This indicates that keeping feature vectors xi,a private to clients does not impact the learning performance significantly. All Fed-PE related algorithms outperform local UCB when T is sufficiently large, demonstrating the effectiveness of communication in improving learning locally. We also set K = 10, d = 4, and vary the number of clients M . The performance of Enhanced Fed-PE is plotted in Figure 1(b). We note that the per-client regret monotonically decreases as M increases, corroborating the theoretical results.
Movielens Dataset: We then use the MovieLens-100K dataset (Harper and Konstan, 2015) to evaluate the performances. Motivated by Bogunovic et al. (2021), we first complete the rating matrix R = [ri,a] ∈ R943×1682 through collaborative filtering (Morabia, 2019), and then use non-negative matrix factorization with 3 latent factors to get R = WH , where W ∈ R943×3, H ∈ R3×1682. Let xi,a be the ith row vector of W . We apply the k-means algorithm to the row vectors of H to produce K = 30 groups (arms), and let θa be the center of the a-th group. Finally, we randomly choose M = 100 users’ feature vectors. We observe that 0.4 ≤ ‖xi,a‖2 ≤ 0.8, and the suboptimality gaps lie in [0.01, 0.8]. The regret performances of the algorithms are plotted in Figure 1(c). The curves show similar characteristics as in Figure 1(a). These results demonstrate the effectiveness of collaborative learning in the federated bandits setting.

heading: 7 Discussion and Conclusion
text: In this work, we have considered a novel federated linear contextual bandits model, which naturally connects local stochastic MAB models with linear contextual bandits through common global parameters. While each client can only observe a projection of each global parameter in its own subspace, Fed-PE utilizes the geometric structure of the local estimates to reconstruct the global parameters and guide efficient collaborative exploration. Theoretical analysis indicates that Fed-PE achieves near-optimal regret for both disjoint and shared parameter cases with a communication cost in the order of O(log T ).
An interesting open question is whether we can further reduce the communication cost without downgrading the regret performance. In particular, we note the the original single-player G-optimal design allows for a sparse solution whose support is of size d(d+1)/2. Our numerical results indicate that such sparse solutions exist for the multi-client G-optimal design as well. Utilizing the sparsity of the solution may reduce the communication cost significantly. Theoretical characterization of the existence of such sparse solutions is our next step.
Another possible direction to explore is to incorporate the differential privacy mechanism to the Fed-PE framework. Although local feature vectors {xi,a} are kept private under Fed-PE, local estimate θ̂i,a lies in range(xi,a), thus revealing the direction of xi,a to the central server. We aim to add certain perturbation on θ̂i,a in order to obfuscate the direction information without significantly affecting the regret performance.
Acknowledgments and Disclosure of Funding
The work of RH and JY was supported by the US National Science Foundation under Grants CNS-1956276, CNS-2003131, CNS-2114542, and ECCS-2030026. CS acknowledges the funding support by the US National Science Foundation under Grants ECCS-2029978, ECCS-2033671, and CNS-2002902. WW’s work was done before he joined Facebook.

Review summaries:
The paper studies a federated setup where each client faces a stochastic contextual bandit.  The parameters of the bandits are coupled across the clientsm. The clients share their local estimates with the server which aggregate them and share back with the clients. The client are hetogenous and the server leverages the geomereic structure of the linear rewars to facilitates the clients learn the optimal arms.  Authors propose a collaborative algorithm called Fed-PE that do not require the nodes to exchange the local feature vectors thus preserving the privacy of the nodes. The authors consider the cases where the paramers are disjoint across the arms or coupled. The performance of the algorithms are valued on both synthetic and real datasets. 

This paper considers a linear contextual bandits problem in a federated setting where different users have different contexts but the rewards are generated through common global parameters. The goal is to minimise the regret for each user while sharing the exploration across users. The paper proposes an algorithm termed Fed-PE with regret bounded as O(\sqrt{dMT}), where d is the dimension of contexts, M is the number of users and T is the time-horizon. A matching lower bound is also presented showing that this regret is tight. The main challenge in this setting is to \emph{balance} the exploration across users as contexts might be very diverse. In order to address this challenge the paper proposes a new G-optimal design which optimises for balanced exploration across users. 

This paper discusses the federated multi-armed bandit problem with linear reward functions. The formulation utilizes a linear structure with agent-dependent contexts to model differences between agent reward functions. The authors propose an algorithm based on a multi-agent G-optimal design with competitive theoretical performance under heterogeneity. The authors additionally establish minimax rates under a reasonable collinearity assumption. The algorithms FedPE and Enhanced FedPE additionally provide competitive experimental performance on both simulated and real-world benchmarks. In summary, the paper is a good contribution, however, there are some issues that I would like to see addressed as well.

I think the paper sufficiently characterizes the federated linear contextual bandits problem (i) it proposes an algorithm for both the shared parameter and disjoint parameter setting (ii) proves regret guarantees and (iii) characterizes a reasonable class of policies for which a lower bound holds; this lower bound shows that the achieved regret guarantees are near optimal. The communication cost of the algorithm is also reasonable w.r.t to prior work. I think the writing of the paper can be improved in some areas and some technical points need to be explained more, but over all I think this is a good paper.