# A Federated Generalized Linear Model for Privacy-Preserving Analysis
## 3 Results and Discussion
In this setup, the server-node communication is very efficient, since there is practically no overhead caused by network operations. In real life, said overhead is very likely to be larger due to low internet connection speeds, varying network infrastructures, etc., yielding a slower execution [31]. Moreover, the data used for the validation were generated artificially with relatively simple relations between variables. Real-life data can be much more complex, which can cause the algorithm to take a larger number of iterations to converge (with the possibility of not converging at all). There are several ways that the presented federated GLM algorithm can be used in real-life analyses. If the parties have an FL infrastructure up and running, they could either implement it from scratch according to their particular needs (based on the description given on Section 2.2) or they could use our provided implementation. As mentioned earlier, this was completed in R. However, it can be used in either R or Python through the provided wrappers. If the parties do not have an FL infrastructure, the easiest way is to use it as part of vantage6. An exhaustive description of the platform is given by [PERSON] et al. [29] and [PERSON] et al. [32], while its documentation can be found on its website [[https://vantage6.ai/](https://vantage6.ai/)]([https://vantage6.ai/](https://vantage6.ai/)) (accessed 14 June 2022). Our federated GLM is already being used in real-life analyses. For example, it has been applied by [PERSON] et al. as a logistic regression to identify women with early stage cervical cancer at low risk of lymph node metastasis. This subpopulation of women is very specific and the number of incidences tends to be quite low, making drawing conclusions from a small patient cohort difficult. In said study, the authors have used our federated GLM to generate a logistic regression that uses data from three different parties (namely, cancer registries across three different European countries). This way, they increased the number of patients for their analysis and generated a more powerful model that supports identifying women with early stage cervical cancer with a low risk of lymph node metastasis, allowing for a more conservative treatment [33; 34]. In another interesting use case, [PERSON] used our federated GLM as a basis for performing stratified propensity score matching [35] (which in turn reduced confounding bias by indication) between breast cancer subpopulations of two international cancer registries. Afterwards, the authors compared quality indicators between them--all of it without having to pool data together [36]. Needless to say, these are only a couple of examples where our presented  
Figure 3: Execution time for the different families (linear, Poisson, logistic) of the GLM. In this case, the number of records was kept constant (at 10,000), while the number of parties was 2, 3,..., 10. Each scenario was simulated 100 times. Data points represent the mean of the execution time, while error bars represent \(\pm 1\) standard deviation. The lines of each family were slightly shifted along the \(x\)-axis to avoid overlap between them. federated GLM has been used. Its applications can go well beyond oncology or health care, after all. There are a few aspects of our federated GLM that could be improved. For example, decreasing communication overhead is known to be a bottleneck for all FL applications [37]. Communication could be more efficient using parallelism in each training round [23] or by actively managing parties' contributions based on the status of their conditions (e.g., network speeds, time required for local updates) [38]. Model update time could also be decreased by transmitting only part of the updated local model by the parties [39].