Another type of attack that has received significant attention in recent mobility research is re-identification attacks [111, 50]. The fundamental concept behind such attacks is that a malicious service provider could exploit background knowledge to associate anonymized user traces with their respective owners, thereby compromising users' anonymity. To address this issue, Khalfoun et al. proposed a federated protocol for assessing the risk of re-identification on mobility data. This protocol involves training a re-identification model in a federated manner using users' traces and subsequently utilizing this model to select the optimal combination and hyperparameters of location privacy protection mechanisms (LPPMs) that can protect a user's privacy whenever they transmit their data to an untrusted Mobile Crowd Sensing Server (MCS). Notably, this solution appears to be the only privacy risk assessment mechanism for mobility data that does not necessitate the presence of a trusted curator, to the best of our knowledge. For a full survey of privacy and security techniques in FL see [125].

Based on our established taxonomy, an analysis of relevant studies, and a meta-analysis, we have observed that some key factors in certain tasks (e.g., privacy and non-iidness in recommendations, resource constraints and computational costs in TFP) are often not given sufficient consideration. Instead, other criteria are erroneously deemed more significant. For instance, in TFP, privacy is typically not a primary concern because the data collected from sensors is generally aggregated, however, current works have considered it more often than other criteria. Moreover, there are several still open-problems as well as a lack of standardization, both in data and code. In light of these observations, we outline a proposed roadmap in the following section. This roadmap is designed to accelerate progress in FL-ST, with a focus on bridging the gap with real-world applications, which are currently underrepresented in the literature. We believe that this approach will not only streamline research in this field but also facilitate its practical implementation.

# 5 Discussion and open research challenges

Based on the review of the above papers, we see the following opportunities and roadmap for the research community to explore.

## 5.1 Semantic Location Embedding and Context-Awareness Modelling

One of the biggest opportunities that we see in continuing research on location and point trajectory predicting, is in regards to integrating more semantic and contextual information about types of places instead of focusing primarily on coordinates. For example, this contextual information can include information on whether a point in trajectory represents someone's workplace, their frequently visited locations, or a potential point of interest in a new town. While this is not limited to FL applications and previous works have often used open source maps to infer information about the type of places (e.g., popularity, socio-economic level), FL can bring a new level of anonymity and personalization to this integration. The semantic representation of the locations can then be learned over time on users' devices, maintaining users' privacy while allowing for better-personalized models.

## 5.2 Byzantine Resilient Spatio-Temporal Mobility Federated Learning

Within the realm of ML, Byzantine Resilience commonly denotes the ability to train an accurate statistical model amid the presence of arbitrary behaviors, commonly referred to as Byzantine users [59]. These behaviors manifest either due to faults or malicious users, encompassing scenarios like a compromised sensor in the TFP problem or a user aiming to promote specific items in a Top-K POI recommendation use case. In the best-case scenario, the trained model proves unusable, resulting in a cost loss incurred during its training. However, in the worst-case scenario, if the attack goes undetected (e.g., backdoors on GNN [193]), the model might exhibit concealed yet malicious behaviors. This predicament poses a significant challenge owing to the difficulty of interpreting model parameters.

Distinguishing between a Byzantine model update transmitted by a client or sensor and an honest but out-of-distribution client often presents a non-trivial task. This complexity amplifies in highly heterogeneous configurations typical of FL-ST mobility applications. Its pertinence highlights this challenge as a substantial research track for Distributed ML and FL. Surprisingly, this aspect remains relatively unexplored in the context of FL-ST mobility applications. Among the multitude of reviewed papers, only Meese et al. [119] and Tang et al. [153] have delved into such adversarial settings. However, neither of these works has considered the novel solutions proposed in this field, such as bucketing [66] and variance reduction techniques [41]. This gap is more concerning given the pronounced heterogeneity characteristic of these applications.

Consequently, there is an urgent imperative to delve into this challenge and contemplate environments accommodating Byzantine behaviors within FL clients. This pursuit should be seen as a fundamental step to bridge the gap between
research efforts and real world implementations. Specifically, it necessitates a comprehensive study and evaluation of existing Byzantine-resilient distributed learning algorithms, a deep understanding of their limitations, and subsequently adapting them to suit the inherent heterogeneity and dynamic evolution within the spatio-temporal context.

## 5.3 Communication Efficiency

The race to enhance the performance of machine learning (ML) models is driving exponential growth in their number of parameters across various fields. In the realm of ST mobility models, where the necessity to capture diverse dimensions (spatial, temporal, preferential, and characteristic) is paramount, this growth becomes even more pronounced. A striking illustration of this trend is the substantial increase of over 4000% observed between ARIMA, a parametric model, and recent GNNs, which have parameters in the order of hundreds of thousands (see Table 7).

Traditionally, in classical ML, concerns centered around the costs associated with training these models and the extensive data required for such endeavors. However, the emergence of FL has shifted the focus towards apprehensions about the expenses related to communicating and transmitting these increasingly expansive models. Surprisingly, a notable observation is that the majority of existing works do not adequately consider these challenges within their frameworks. That is, they do not consider both the cost of training as well as the cost of collecting these models by the central server.

Recognizing the significance of this issue, particularly in light of its comprehensive exploration in other fields, we advocate for heightened attention from the research community. We urge future work to delve into existing solutions like pruning [79] and quantization [91], emphasizing the need to quantify the cost-effectiveness of these approaches in the context of FL-ST mobility.

## 5.4 Trust, Fairness, and Accountability

In addition to trust and accountability, another challenge that we see spatial temporal mobility models will face under a federated setting is fairness. *That is to what extent the models that are trained on location traces are equitable?* Especially models that are designed for the purpose of mobility flow prediction and allocation of transport options. For instance, mobility demand prediction algorithms have been shown to offer higher service quality to neighborhoods with more white people [23]. Indeed, as recent evidence from the broader machine learning domain has shown, the systematic discrimination in making decisions against different groups has been shifted from people to autonomous algorithms [81, 68]. In many applications, discrimination may be defined by different protected attributes, such as race, gender, ethnicity, and religion, that directly prevent favorable outcomes for a minority group in societal resource allocation, education equality, employment opportunity, etc [146]. Measuring fairness of mobility models is a dimension that has been vastly overlooked in applications of spatial-temporal mobility models, with exception of a few works [169, 168, 53] and with little consensuses on how fairness should be defined and measured for spatial-temporal applications. One way of controlling for fairness of mobility models under the FL setting is to create auditing systems that can infer information about the training without having access to location data of the devices or the global model at the FL server [115, 113]. We believe future work will focus on dynamic middle-wares that can leverage solutions such as clustered FL to offer interpretability of the underlying models [195, 55, 93] are crucial to transition exiting solutions from research to practice.

## 5.5 Standardisation and Reproducibility

As the landscape of spatio-temporal mobility research continuously evolves, it is increasingly crucial to establish a common ground for assessing its advancements. This involves not only pinpointing the most effective proposed solutions but also identifying the persistent challenges and shortcomings. Achieving this necessitates standardized datasets, uniform methods for data preprocessing and splitting, and ideally, a code base or metadata ensuring the reproducibility of each solution. Regrettably, similarly to other domains like recommendation systems [30, 31], this practice is seldom followed.

For instance, in studies related to the TFP task, there is a prevalent use of diverse datasets, sometimes from the same source but across disparate timeframes, different space and time discretizing methods as well as unclear distinct splitting methods. These splits often rely on various sampling techniques. Yet, as underlined in [122], the process of partitioning data into training, testing, and potentially validation sets significantly influences the measured performance. Another example, specifically in Top-K POI recommendation, involves the utilization of metrics based on parameters (e.g., varying K values for accuracy at rank K). In FL, this challenge exacerbates due to the proliferation of parameters impacting the results (e.g., the number of clients, sampling strategies, aggregation techniques).

Consequently, robustly comparing different works, even those evaluated on identical datasets, becomes impossible. This is likely why most reviewed works unfortunately lack substantial comparisons with their federated counterparts.
research efforts and real world implementations. Specifically, it necessitates a comprehensive study and evaluation of existing Byzantine-resilient distributed learning algorithms, a deep understanding of their limitations, and subsequently adapting them to suit the inherent heterogeneity and dynamic evolution within the spatio-temporal context.

## 5.3 Communication Efficiency

The race to enhance the performance of machine learning (ML) models is driving exponential growth in their number of parameters across various fields. In the realm of ST mobility models, where the necessity to capture diverse dimensions (spatial, temporal, preferential, and characteristic) is paramount, this growth becomes even more pronounced. A striking illustration of this trend is the substantial increase of over 4000% observed between ARIMA, a parametric model, and recent GNNs, which have parameters in the order of hundreds of thousands (see Table 7).

Traditionally, in classical ML, concerns centered around the costs associated with training these models and the extensive data required for such endeavors. However, the emergence of FL has shifted the focus towards apprehensions about the expenses related to communicating and transmitting these increasingly expansive models. Surprisingly, a notable observation is that the majority of existing works do not adequately consider these challenges within their frameworks. That is, they do not consider both the cost of training as well as the cost of collecting these models by the central server.

Recognizing the significance of this issue, particularly in light of its comprehensive exploration in other fields, we advocate for heightened attention from the research community. We urge future work to delve into existing solutions like pruning [79] and quantization [91], emphasizing the need to quantify the cost-effectiveness of these approaches in the context of FL-ST mobility.

## 5.4 Trust, Fairness, and Accountability

In addition to trust and accountability, another challenge that we see spatial temporal mobility models will face under a federated setting is fairness. *That is to what extent the models that are trained on location traces are equitable?* Especially models that are designed for the purpose of mobility flow prediction and allocation of transport options. For instance, mobility demand prediction algorithms have been shown to offer higher service quality to neighborhoods with more white people [23]. Indeed, as recent evidence from the broader machine learning domain has shown, the systematic discrimination in making decisions against different groups has been shifted from people to autonomous algorithms [81, 68]. In many applications, discrimination may be defined by different protected attributes, such as race, gender, ethnicity, and religion, that directly prevent favorable outcomes for a minority group in societal resource allocation, education equality, employment opportunity, etc [146]. Measuring fairness of mobility models is a dimension that has been vastly overlooked in applications of spatial-temporal mobility models, with exception of a few works [169, 168, 53] and with little consensuses on how fairness should be defined and measured for spatial-temporal applications. One way of controlling for fairness of mobility models under the FL setting is to create auditing systems that can infer information about the training without having access to location data of the devices or the global model at the FL server [115, 113]. We believe future work will focus on dynamic middle-wares that can leverage solutions such as clustered FL to offer interpretability of the underlying models [195, 55, 93] are crucial to transition exiting solutions from research to practice.

## 5.5 Standardisation and Reproducibility

As the landscape of spatio-temporal mobility research continuously evolves, it is increasingly crucial to establish a common ground for assessing its advancements. This involves not only pinpointing the most effective proposed solutions but also identifying the persistent challenges and shortcomings. Achieving this necessitates standardized datasets, uniform methods for data preprocessing and splitting, and ideally, a code base or metadata ensuring the reproducibility of each solution. Regrettably, similarly to other domains like recommendation systems [30, 31], this practice is seldom followed.

For instance, in studies related to the TFP task, there is a prevalent use of diverse datasets, sometimes from the same source but across disparate timeframes, different space and time discretizing methods as well as unclear distinct splitting methods. These splits often rely on various sampling techniques. Yet, as underlined in [122], the process of partitioning data into training, testing, and potentially validation sets significantly influences the measured performance. Another example, specifically in Top-K POI recommendation, involves the utilization of metrics based on parameters (e.g., varying K values for accuracy at rank K). In FL, this challenge exacerbates due to the proliferation of parameters impacting the results (e.g., the number of clients, sampling strategies, aggregation techniques).

Consequently, robustly comparing different works, even those evaluated on identical datasets, becomes impossible. This is likely why most reviewed works unfortunately lack substantial comparisons with their federated counterparts.
Moreover, considering that the distinct experimental procedures often stem from stochastic processes (client and data point sampling, stochastic learning), we stress the necessity for statistical tests to gauge the statistical significance of observed results.

We acknowledge that research contexts rarely align perfectly. Nevertheless, the need for standardized datasets and preprocessing methodologies in the field is undeniable. Existing surveys [196, 48] have proposed solutions in a centralized context. While the direct applicability of these solutions to FL may require adaptation, they provide valuable insights and methodologies that can serve as foundational building blocks for the FL paradigm. Moreover, there have been several software efforts [175, 62, 57, 132, 127, 19, 12] that furnish a set of libraries for standardized spatio-temporal data analysis, preprocessing, and visualization. Using these as building blocks might represent an essential step towards standardization and reproducibility in the field.

## 5.6 Realistic Cross-silo Spatial Temporal Datasets for Benchmarking

Existing approaches that we reviewed are mostly evaluated with ST data partitioned artificially. Nevertheless, the long-term development of this field still requires realistic and large-scale federated datasets to be made available to support experimental evaluations under settings close to practical applications. For instance, in the reviewed literature, there is a lack of research on how the geographic distribution of silos can lead to a geographically distinct flow of information. Establishing policy-based scenarios in order to guide how the data should be partitioned across silos to reflect real-world data ownership challenges is a direction that we believe the research community will be working closely with other stakeholders in the future.

## 5.7 Transition to Real-world Deployment Through Dedicated Frameworks

Finally, we believe that just as crowd-sensing research was successful a decade ago through frameworks such as AWARE [45], which reduced the burden of app development for data collection, frameworks specifically designed for federated mobility models will facilitate the transition from limited research to in-wild deployments. To achieve this transition, it is crucial to i) provide benchmarks for mobility applications and ii) develop mobility-centered federated learning frameworks, as was the case for graph applications [64] and IoT applications [187]. This will allow the research community to effectively evaluate and compare the performance of federated learning models on mobility data. We foresee that the transition between the current research efforts to real deployment will happen over stages where first multi-disciplinary research will focus on understanding users' attitudes towards using their location data for training models. After all, similar research on crowd-sensing applications has shown that location information is a top concern of users' involvement in these applications [3]. To the best of our knowledge, currently, there are no existing works in understanding users' privacy concerns when their data is not shared externally but is still used in creating predictive models. As a next step, we envision a slow transition between fully centralized models to decentralized models. Rather than training models focused on end-user prediction tasks, generative models that allow synthetic trace generation by learning from user mobility traces will be used to update and de-bias centralized datasets.

# 6 Conclusion

In this paper, we surveyed the federated learning models in the domain of mobility prediction as well as the widely used datasets for spatial-temporal models. We described the challenges that exist in applying common deep learning techniques in decentralized settings and discussed the opportunities for the research community to consider for future work. Our work indicates rapid growth and interest in this space, with promising future directions both in terms of theoretical frameworks and models, and practical applications and use cases. We hope both academics and practitioners find this survey useful for choosing the appropriate approach for their individual scenarios.

## Acknowledgement

The publication of this work was made possible by generous support from the US National Science Foundation (NSF) award IIS-2304213.
Moreover, considering that the distinct experimental procedures often stem from stochastic processes (client and data point sampling, stochastic learning), we stress the necessity for statistical tests to gauge the statistical significance of observed results.

We acknowledge that research contexts rarely align perfectly. Nevertheless, the need for standardized datasets and preprocessing methodologies in the field is undeniable. Existing surveys [196, 48] have proposed solutions in a centralized context. While the direct applicability of these solutions to FL may require adaptation, they provide valuable insights and methodologies that can serve as foundational building blocks for the FL paradigm. Moreover, there have been several software efforts [175, 62, 57, 132, 127, 19, 12] that furnish a set of libraries for standardized spatio-temporal data analysis, preprocessing, and visualization. Using these as building blocks might represent an essential step towards standardization and reproducibility in the field.

## 5.6 Realistic Cross-silo Spatial Temporal Datasets for Benchmarking

Existing approaches that we reviewed are mostly evaluated with ST data partitioned artificially. Nevertheless, the long-term development of this field still requires realistic and large-scale federated datasets to be made available to support experimental evaluations under settings close to practical applications. For instance, in the reviewed literature, there is a lack of research on how the geographic distribution of silos can lead to a geographically distinct flow of information. Establishing policy-based scenarios in order to guide how the data should be partitioned across silos to reflect real-world data ownership challenges is a direction that we believe the research community will be working closely with other stakeholders in the future.

## 5.7 Transition to Real-world Deployment Through Dedicated Frameworks

Finally, we believe that just as crowd-sensing research was successful a decade ago through frameworks such as AWARE [45], which reduced the burden of app development for data collection, frameworks specifically designed for federated mobility models will facilitate the transition from limited research to in-wild deployments. To achieve this transition, it is crucial to i) provide benchmarks for mobility applications and ii) develop mobility-centered federated learning frameworks, as was the case for graph applications [64] and IoT applications [187]. This will allow the research community to effectively evaluate and compare the performance of federated learning models on mobility data. We foresee that the transition between the current research efforts to real deployment will happen over stages where first multi-disciplinary research will focus on understanding users' attitudes towards using their location data for training models. After all, similar research on crowd-sensing applications has shown that location information is a top concern of users' involvement in these applications [3]. To the best of our knowledge, currently, there are no existing works in understanding users' privacy concerns when their data is not shared externally but is still used in creating predictive models. As a next step, we envision a slow transition between fully centralized models to decentralized models. Rather than training models focused on end-user prediction tasks, generative models that allow synthetic trace generation by learning from user mobility traces will be used to update and de-bias centralized datasets.

# 6 Conclusion

In this paper, we surveyed the federated learning models in the domain of mobility prediction as well as the widely used datasets for spatial-temporal models. We described the challenges that exist in applying common deep learning techniques in decentralized settings and discussed the opportunities for the research community to consider for future work. Our work indicates rapid growth and interest in this space, with promising future directions both in terms of theoretical frameworks and models, and practical applications and use cases. We hope both academics and practitioners find this survey useful for choosing the appropriate approach for their individual scenarios.

## Acknowledgement

The publication of this work was made possible by generous support from the US National Science Foundation (NSF) award IIS-2304213.
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[1] 2009. The Official Website of the City of New York. https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page. Accessed: 2023-03-01.

[2] 2013. City bike Official Web Page. https://citibikenyc.com/system-data. Accessed: 2023-03-01.

[3] 2018. data usage privacy uk consumers. https://ceo.digital/news/data-usage-privacy-uk-consumers. Accessed: 2023-03-07.

[4] 2019. Machine Learning Helps Payment Services Detect Fraud. https://www.americanexpress.com/en-gb/business/trends-and-insights/articles/payment-services-fraud-detection-using-AI/

[5] Mohammed Akallouch, Oussama Akallouch, Khalid Fardousse, Afaf Bouhoute, and Ismail Berrada. 2022. Prediction and Privacy Scheme for Traffic Flow Estimation on the Highway Road Network. Information 13, 8 (2022), 381. https://doi.org/10.3390/INF013080381

[6] Rasha Al-Huthaifi, Tianrui Li, Wei Huang, Jin Gu, and Chongshou Li. 2023. Federated learning in smart cities: Privacy and security survey. Information Sciences 632 (2023), 833-857. https://doi.org/10.1016/J.INS.2023.03.033

[7] Muhammad Ahmad-ud-din, Elena Ivannikova, Suleiman A. Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, and Adrian Flanagan. 2019. Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System. CoRR abs/1901.09888 (2019). arXiv:1901.09888 http://arxiv.org/abs/1901.09888

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[9] Elli Androulaki, Artem Barger, Vita Bortnikov, Christian Cachin, Konstantinos Christidis, Angelo De Caro, David Enyeart, Christopher Ferris, Gennady Laventman, Yacov Manevich, Srinivasan Muralidharan, Chet Murthy, Binh Nguyen, Manish Sethi, Gari Singh, Keith Smith, Alessandro Sorniotti, Chrysoula Stathakopoulou, Marko Vukolic, Sharon Weed Cocco, and Jason Yellick. 2018. Hyperledger fabric: a distributed operating system for permissioned blockchains. In Proceedings of the Thirteenth EuroSys Conference, EuroSys 2018, Porto, Portugal, April 23-26, 2018, Rui Oliveira, Pascal Felber, and Y. Charlie Hu (Eds.). ACM, 30:1-30:15. https://doi.org/10.1145/3190508.3190538

[10] Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara, and Fedelucio Narducci. 2021. How to put users in control of their data in federated top-N recommendation with learning to rank. In SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, Republic of Korea, March 22-26, 2021, Chih-Cheng Hung, Jiman Hong, Alessio Bechini, and Eunjee Song (Eds.). ACM, 1359-1362. https://doi.org/10.1145/3412841.3442010

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[12] Kay Werner Axhausen. 2007. Definition of movement and activity for transport modelling. In Handbook of transport modelling. Vol. 1. Emerald Group Publishing Limited, 329-343.

[13] Nuria Rodríguez Barroso, Daniel Jiménez-López, María Victoria Luzón, Francisco Herrera, and Eugenio Martínez-Cámara. 2023. Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges. Inf. Fusion 90 (2023), 148-173. https://doi.org/10.1016/J.INFFUS.2022.09.011

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[17] Alastair R. Beresford and Frank Stajano. 2003. Location Privacy in Pervasive Computing. IEEE Pervasive Comput. 2, 1 (2003), 46-55. https://doi.org/10.1109/MPRV.2003.1186725
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# 7 Appendix

## 7.1 Survey Review Methodology

To create the summary of the surveys and their topics, we crawled the citation (bib file) of all the published articles with the keywords 'Federated Learning' and 'Survey' or 'Review' in the title for each year. We used the publish or perish tool for this crawling and used Google Scholar as the platform. We downloaded the abstracts of all these surveys from those papers and fed them to a Large Language Model (GPT 3.5) for thematic categorization of the topics. We followed a similar process for counting the number of papers per year that were published on FL-ST topics (non-surveys).
# 7 Appendix

## 7.1 Survey Review Methodology

To create the summary of the surveys and their topics, we crawled the citation (bib file) of all the published articles with the keywords 'Federated Learning' and 'Survey' or 'Review' in the title for each year. We used the publish or perish tool for this crawling and used Google Scholar as the platform. We downloaded the abstracts of all these surveys from those papers and fed them to a Large Language Model (GPT 3.5) for thematic categorization of the topics. We followed a similar process for counting the number of papers per year that were published on FL-ST topics (non-surveys).