# A Review of Federated Learning in Energy Systems

Xu Cheng  Section of Energy Markets
Smart Innovation Norway
Halden, Norway
<EMAIL_ADDRESS>Chendan Li  Department of Marine Technology
Norwegian University of Science and Technology
Trondheim, Norway
<EMAIL_ADDRESS>Xiufeng Liu  Department of Technology,
Management and Economics
Technical University of Denmark
Produktionstorvet, Denmark
<EMAIL_ADDRESS>
###### Abstract

With increasing concerns for data privacy and ownership, recent years have
witnessed a paradigm shift in machine learning (ML). An emerging paradigm,
federated learning (FL), has gained great attention and has become a novel
design for machine learning implementations. FL enables the ML model training
at data silos under the coordination of a central server, eliminating
communication overhead and without sharing raw data. In this paper, we conduct
a review of the FL paradigm and, in particular, compare the types, the network
structures, and the global model aggregation methods. Then, we conducted a
comprehensive review of FL applications in the energy domain (refer to the
smart grid in this paper). We provide a thematic classification of FL to
address a variety of energy-related problems, including demand response,
identification, prediction, and federated optimizations. We describe the
taxonomy in detail and conclude with a discussion of various aspects,
including challenges, opportunities, and limitations in its energy informatics
applications, such as energy system modeling and design, privacy, and
evolution.

###### Index Terms:

Review, Federated Learning, Energy sector, Distributed learning, Privacy and
security

##  I Introduction

Today the demand for energy is increasing rapidly in the global, due to
population and economic growth. The building sector accounts for 40% of total
energy consumption and 60% of electricity consumption [1]. Distributed energy
resources are a viable solution to address this problem by integrating
distributed energy systems at users. Distributed energy systems have the
characteristics of high efficiency, low loss, low pollution, and flexible
operations. On the other hand, for electricity supply, smart grids have
intensively adopted the Internet of Things (IoT), smart meters, and advanced
communication networks (e.g. 5G) and data management systems, which form an
advanced energy management infrastructure. Smart grids and distributed energy
systems together form a giant energy of the Internet (IoE) to provide stable
and sustainable energy to end users. However, several prominent issues arise
with the provision of various energy services, the management of massive data,
and the offloading of computing workloads from the cloud. First, data privacy
becomes a major concern as customer data is often used to train data-driven
models, e.g., for detecting malicious nodes in IoE. Therefore, it has to
access private customer information, such as consumption profiles and habits,
to train the model. Second, data owners may not be willing to share their data
to a centralized server for model training, mainly due to privacy, commercial
competition, and technical barrier reasons [2]. At the legal level,
governments or organizations around the world are also increasingly committed
to data privacy protection. For example, the European Union has enforced the
General Data Protection Regulation (GDPR) since 2017 [3]. Third, the computing
resource requirements for traditional centralized model training are
relatively high due to the management of large amounts of data sets. Last,
data transmission over the network is vulnerable to cyberattacks and
introduces overhead. Therefore, there is a pressing need for a feasible
solution to address these issues.

Federated learning (FL) is an ideal paradigm, which was first introduced by
Google in 2016 [4]. FL is essentially a distributed learning approach, where
multiple distributed clients train machine learning models separately using
their own local data under the coordination of a central server, then the
server aggregates the trained models into a final global model. Training is an
iterative process that involves updating local models and aggregating the
global model until the model converges or the training reaches the predefined
number of interactions. FL is a major shift from centralized and expensive
machine learning to a distributed manner that can use many distributed
computing resources. This learning paradigm improves data privacy, as the raw
data remain on the local device, eliminating network overhead as only the
model updates are exchanged. In recent years, FL has been used successfully in
industries related to IoT and edge computing, mainly for privacy protection,
but it is attracting more and more interest. Some literature reviews were
performed not only on FL technology itself, such as the different FL
architectures and their learning settings, e.g., [5, 6, 7, 8, 9, 10], but also
its applications, e.g., [11, 12, 13, 14, 15]. However, there is still no
systematic review of FL applications in energy. This is likely due to more
research efforts in areas directly related to the protection of personal
information privacy, such as medicine, healthcare, and insurance. On the
contrary, the literature on FL in energy is much fewer and all of them emerged
within the last two years. However, we have seen that growth is fast and its
applications go beyond addressing data privacy, which is raising great
research attention in the energy community. Therefore, we believe that it is
necessary to conduct a systematic review. In this paper, we will classify and
summarize the different solutions based on FL and their applications in the
energy sector. Moreover, unlike other reviews of FL technologies which focus
on different architectures and complementary technologies to enhance FL, we
pay special attention to different aggregate algorithms to generate the global
model. This review can help advance understanding of FL technology and promote
its use cases in the energy sector.

In summary, this paper makes the following contributions.

  * •

We list the taxonomy of FL technology, classify FL frameworks according to
data partitioning methods and network structures, and, in particular,
summarize the mainstream aggregation algorithms for generating the global
model.

  * •

We review FL use cases in the smart grid domain, identify their technological
features, and provide an overview of current FL in the energy domain.

  * •

We discuss the challenges and opportunities for the FL technology itself and
for applications in energy and suggest possible solutions to shed light on the
wider adoption of FL in energy.

The remainder of this paper is structured as follows. Section II gives an
overview of the FL paradigm, and surveys the methods for global model
generation; Section III reviews the use cases of FL in energy systems; Section
IV discusses the challenges, opportunities, and limitations of FL in energy;
Section V concludes the paper and presents the future research directions.

##  II Federated Learning Concept

###  II-A Overview

FL provides a novel machine learning solution with distributed and enhanced
privacy capabilities for many energy applications, which has the potential to
shape current energy systems [16]. With growing concerns about privacy issues,
FL is particularly attractive for constructing a distributed model by pushing
machine learning functions to the edge of the energy system when the data
reside. Eventually, the data of each energy system is never directly shared
with others under the FL framework, while supporting collaborative learning of
shared global models, which benefits both grid operators and energy users in
terms of network resource savings and enhanced privacy. As such, FL is a
powerful alternative to traditional centralized machine learning approaches.

Figure 1 illustrates the concept of FL, where there are two entities: clients
and a central server. The local models can be the end users of an energy
system, and the central server is used to aggregate the local models trained
by the clients. The general FL process comprises the following three steps:

Figure 1: Overview of federated learning applied in an energy system

  1. 1.

System initialization and device selection: Most recent work assumes that all
clients participate in each training round in the FL process. However, in each
training round, only a certain number of clients are selected in practice.

  2. 2.

Local training: When training starts, the central server initializes a global
model and distributes it to all selected clients. The clients $i$ then start
local training using their own local data $d_{i}$ and calculate the gradient
$\omega_{i}$ by minimizing a loss function $F(\omega_{i})$:

$\omega_{i}^{*}=argminF(\omega_{i})$ (1)

Note that the loss function varies between different FL algorithms depending
on the relationship between the input and output pairs.

  3. 3.

Model aggregation: After collecting all model updates from the clients, the
server will calculate a new global model as :

$\omega_{S}=\frac{1}{\sum_{i\in N}|d_{i}|}\sum_{i=1}^{N}|d_{i}|w_{i}$ (2)

where $\omega_{S}$ represents the global model. After deriving the global
model on the server, the server broadcasts the new $\omega_{S}$ model to all
clients in order to update the old client models. The whole learning process
ends when the desired accuracy is reached.

###  II-B Types

Based on our review of recent advances in FL in the energy domain, we classify
its types from the data partitioning and network structure perspectives.

####  II-B1 Data partition

Depending on how the training data is distributed in the sample space and
feature space, FL can be classified into three categories: horizontal FL,
vertical FL and federated transfer learning.

  * •

Horizontal FL: In horizontal FL, the training data of the clients have the
same feature space but different sample spaces. Due to this settings, clients
can use the same machine learning model for jointly training the global FL
model. To ensure security, the local gradient of the clients usually needs to
be protected by encryption techniques [9].

  * •

Vertical FL: Vertical FL emphasizes shared global model learning in a network
of clients with the same sample space and different data feature spaces [17].
In vertical FL, a specific alignment method should be designed to collect
overlapping data samples from clients. These samples are combined to train a
general machine learning model using encryption techniques.

  * •

Federated transfer learning: Federated transfer learning is designed with the
aim of extending the sample space in the vertical FL architecture to more
learning clients for data with different sample spaces and different feature
spaces [18]. In the concept of federated transfer learning, different feature
spaces should be transformed into the same representation to aggregate data
from multiple clients. To protect data privacy, encryption techniques are also
usually widely used.

####  II-B2 Network structure

Depending on the network topology, FL can be divided into two categories:
central server-based FL and distributed FL.

  * •

Central server-based FL: Central server-based FL is one of the most popular FL
architectures. Fig. 1 is a typical structure of the central server-based FL.
In this type of FL, there is a central server and a set of clients that
execute the FL model. During a training round, all clients participate in the
training of the network model in parallel using their own data. All clients
then transmit the trained parameters to the central server, which aggregates
these parameters using a weighted average algorithm such as FedAvg. Then, the
computed global model is sent back to all clients for the next training
iteration. At the end of the training process, each client receives an
identical global model and its personalized model. This type of FL is highly
dependent on the server model to coordinate the aggregation and distribute
model updates.

  * •

Distributed FL: Distributed FL is a network topology without any central
server to coordinate the training process. Instead, all clients are connected
in a peer-to-peer (P2P) manner to perform AI training [19]. In this way, in
each round of communication, clients are also trained locally on their own
data. Each client then implements model aggregation using model updates
received from neighboring clients via P2P communication to agree on global
updates. A distributed FL is designed to completely or partially replace
central server-based FL when communication with the server is unavailable or
when the network topology is highly scalable. Due to its contemporary
features, a distributed FL can be integrated with P2P-based communication
technologies such as blockchain to build a decentralized FL system. In this
way, distributed FL clients can communicate through a blockchain ledger where
model updates can be offloaded to the blockchain for secure model exchange and
aggregation.

###  II-C Aggregation algorithms

Aggregation algorithm plays a key role in FL [8]. It incorporates the updates
of all local models from all participating clients. The aim of the aggregation
algorithm is to: 1) enhance privacy from local model updates; 2) preserve
communication bandwidth; 3) facilitate asynchronous updates from clients. As
the core of FL, the implementation of the aggregation algorithm varies
according to the pre-defined needs. The current most widely used aggregation
algorithms can be summarized as follows.

  * •

FedAvg: This algorithm was originally proposed by Google. In FedAvg, the
central server as the coordinator initiates the FL training process by sharing
global parameters or global models with each selected client. Each selected
client uses local data to train a global model and shares the weights of the
trained model with the central server. The update of the global model is
calculated by averaging the local models collected on the central server [20].

  * •

FedProx: This algorithm is an improved version of FedAvg, which is proposed to
address heterogeneity in FL [21]. FedProx takes into account variations in
computing power and different factors in devices participating in FL training
rounds. FedProx also introduces a proximal term to handle inconsistencies in
local updates. The results of the experiment [21] indicate that FedProx can
achieve positive results in heterogeneous settings.

  * •

FedMA: This algorithm is used to build a shared model to aggregate CNN and
LSTM models in FL [22]. FedMA averages the model on the central server through
hierarchical matching and averaging hidden elements, such as neurons and
channels in the neural network. As shown in the experimental results, FedMA
works well on heterogeneous clients, surpassing FedAvg and FedProx in several
rounds of training.

##  III Use cases

Electrification plays a key role in the whole energy landscape as the most
efficient and environment-friendly final energy form with higher
controllability due to advances in many technologies, such as materials, power
electronics, information and communication technology (ICT), etc. In this
section, we will focus on FL applications in energy (most of which are in the
smart grid). As a particular data-driven model, technology innovation will
only bring value when it fits into a particular use case. Therefore, we will
first briefly introduce the general conditions of the smart grid domain, which
is under dramatic changes. The main players that shape the landscape of the
smart grid will be identified. Next, emerging FL applications based on the
current literature review will be summarized from a use case perspective,
along with their particular technology contributions. Special attention is
paid to the aggregation mechanism for each FL application, as different use
cases usually need customized FL frameworks with specific aggregation models.

Figure 2: Stakeholders in the Smart Grid

###  III-A Smart Grid landscape in the IoE and its stakeholders

The power system witnesses radical revolutions featured by the
“3D”–decolonization, decentralization, digitization, and it renders the system
into a smart grid. Traditional stakeholders in the power sector are also
changing due to the structural change from a vertical transmission of the
energy starting from large power plants to end users through transmission
lines, to a system which is networked with distributed energy resources. At
the same time, more and more new players are entering the energy sector with
an increasing deregulated and competitive electricity market. Among these new
players, the interests of the prosumers who both consume and generate energy
with distributed generations are becoming increasingly non-trivial.

As electricity continues to replace other energy forms as the final energy
usage type, the interaction between the power sector and other energy sectors
becomes more significant. It is no exaggeration to say that the smart grid
will be the central enabler in the ecosystem of IoE, with many emerging
technologies and applications, such as microgrids as the energy hubs, data
analysis, multi-energy flow energy management, etc[23, 24, 25]. Fig. 2 depicts
the current smart grid landscape that engages with different stakeholders in
dimensions including interoperability, domains, and zones.

###  III-B Use case summary

FL is an emerging research field in the energy sector. From a data analysis
point of view, applications can be divided into analysis, prediction, and
decision making (control and optimization). In this paper, we review the FL
use cases for energy systems and summarize in Table I according to the use
scenarios, the stakeholders benefited, challenges, FL types, and the
aggregation feature of FL.

TABLE I: Summary of federated learning for energy systems Ref. |  Scenarios |  Stakeholder |  Challenges |  FL type |  Aggregation feature
---|---|---|---|---|---
[26] |  Household characteristic identification, such as income level, flat type, floor area, number and type of applicance and number of residence |  Retailers |  Data ownership, privacy preservation |  Horizontal, central server-based FL |  Asynchronous stochastic gradient descent with delay compensation (ASGD-DC)
[27] |  Energy management of multiple smart homes equipped with DER in a system with one home energy management and several local home energy systems. |  Aggregator of LHEMSs |  computation burden, timeliness/real time, privacy preserve |  FRL, horizontal |  FedSGD algorithm
[28] |  Residential building load forecasting with data from IoT devices |  End user(smart home owner) |  data access, privacy preserve, cloud service cost |  Horizontal, decentralized FL |  Decentralized, global server free
[29] |  Load forecasting using smart meter data |  utility |  computation burden, data ownership, privacy preserve |  horizontal, Central server based FL |  FedAVG
[30] |  Market participate and energy exchange with prosumer community group |  Aggregator-prosumer community groups, end user |  privacy preserve, data access |  Horizontal, central server based FL |  Averaging
[31] |  Identificaiton of socio-demographic characteristics of electricity consumers from smart meter data |  load forecasting using smart meter data |  privacy preserve |  Horizontal, central server based FL |  Model averaging
[32] |  Demand response through load control of multiple households to reduce peak load and user cost |  Aggregator, end user(residential) |  privacy preserve |  Horizontal, central server based FL, RL |  decentralized algorithm based on proximal Jacobian alternating direction method of multipliers (PJ-ADMM)
[33] |  Behind-the-meter solar disaggregation from the net load to the solar generation profile at the community level |  Utility |  Privacy preserve |  Horizontal, central server based FL |  A layerwise parameter aggregation strategy
[27] |  Home Energy management System(HEMS) of multiple smart homes equipped with DER. |  Aggregato (HEMS that consists of a single GS and N LHEMSs) |  computation burden, real time, privacy preserve |  FRL, horizontal |  FedSGD algorithm
[2] |  Blade icing detection for wind turbines in the wind farms |  wind farm owners |  data ownership, computation burden |  Horizontal, central server based FL |  Differentiated computational capabilities in server and client models
[34] |  Spatiotemporal scenario generation of renewable energies |  Utility |  privacy preserve |  Horizontal, central server based FL |  Integrating federated learning and least square generative adversarial networks
[35] |  Probabilistic prediction of energy demand and driving range for electric vehicles to deal with limited driving range in a sparse charging infrastructure |  EV owner |  privacy preserve, computation and communication overhead |  Horizontal, central server based FL |  Federated Learning With Clustering, FedAvg and FedAG
[36] |  Energy theft detection in smart grid |  Utility |  privacy preserve |  Horizontal, central server based FL |  FL with homomorphic encryption
[37] |  Residential short-term energy consumption forecasting |  Utility |  privacy preserve |  Horizontal, central server based FL |  FedAVG
[38] |  Non-intrusive load monitoring |  Utility |  data access, privacy preserve |  Horizontal, central server based FL |  FedAVG
[39] |  Predicting energy demand for electric vehicle (EV) networks |  Grid operator |  privacy preserve |  Horizontal, central server based FL |  Clustering-based FL
[40] |  Anomaly Detection in Smart Buildings |  End user-the owner of the smart buildings |  computation burden,timeliness, |  Horizontal, central server based FL |  FL with encryption(PySyft)
[41] |  False data injection attack detection on power grid state estimation |  Utility-power system state owner, detection service provider (DSP) |  data access, privacy preserve, data ownership |  Horizontal, central server based FL |  Federated learning with incentive

##  IV Challenges and opportunities

FL is a new paradigm of distributed learning across different devices, data
centers, and geographic locations. It is also a multidisciplinary learning
framework that involves several disciplines, including cryptography,
databases, and machine learning. Among many others, the challenges and
opportunities of FL are summarized as follows: 1) How to improve the
efficiency of the communication between the central server and the clients,
which mainly involves how to reduce the number of communications and compress
the data. 2) How to protect the security of the data on the communication
during the model training process. In this regard, data encryption techniques
can be applied, including homomorphic encryption and differential privacy. 3)
How to partition the data on the client side to achieve optimal parallel
learning efficiency. Existing studies focus on the application of horizontal
and vertical data partitioning strategies. 4) How to deal with non-identical
distribution problems of the training data. These can be problems of skewed
feature distribution and/or skewed label distribution. 5) How to improve
learning efficiency, which mainly consists of studying how to optimize the
machine learning algorithm and accelerate model convergence. 6) How to achieve
multitask learning and personalized learning in a federated setting. Since
clients may have different models, settings, and devices, this heterogeneity
determines the challenges of balancing parallel tasks, thus providing
personalized learning opportunities. 7) How to design machine learning
algorithms and tune hyperparameters in an FL framework.

The above are the main challenges and opportunities for FL technology itself,
which has been the subject of many research efforts. As a result, this has led
to a large number of review articles; in this paper, we instead have focused
on the other aspect that we think is most interesting, but has not yet been
examined, namely, the global model generation algorithms. Aggregation
algorithms are directly related to the performance of the final model, such as
generality. In addition, we believe that FL should be fully decentralized or
support peer-to-peer distributed learning, similar to blockchain technology
where learning is consensus-based. Finally, due to the complexity of
implementing FL technology and the applications it supports, we believe it
would be beneficial to integrate visualization into an FL framework, which can
greatly lower the learning curve and improve the interpretability of machine
learning models implemented in an FL framework.

FL has a wide range of potential in the field of energy, which can be
summarized as follows. First, it is often difficult to create powerful data-
driven models for renewable energy prediction, mainly because of inherent
stochasticity. For example, wind power generation is affected by wind speed,
and solar photovoltaic energy is affected by the intensity and timing of solar
exposure. Training a forecasting model with high accuracy and generality
requires different data sources, which may be from power plants at different
geographical locations. However, these data may not be available due to data
ownership or commercial competition, in which case FL becomes essential to
create a collaborative prediction model. Second, in the residential building
sector, FL can help protect the privacy of customer information. For example,
in Europe, the residential electricity supply is often operated by different
distributors, each of which has its own customers. In order to create data-
driven models from energy consumption and customer information, FL can address
the model in a non-sharing environment with data privacy guarantee. This makes
it possible to provide personalized energy services, such as energy-efficiency
product recommendations, demand-response programs, and customized energy
management solutions. Although research has been conducted on personalized
recommendations based on FL in other fields, its applications in the energy
sector remain an open problem that is interesting to investigate. Finally,
energy involves many distributed optimization problems, including production,
transportation, and distribution. The advantage of applying federated-based
optimization is that it supports heterogeneous models, i.e., the model
structure does not need to be consistent across different nodes. Therefore, an
ensemble-like heterogeneous distributed optimization model can be established
to help with decision making regarding energy planning or supply.

We are aware that energy is a very broad concept that has many subdomains. For
example, depending on the types of energy, it can be divided into water,
electricity, gas, heat, etc., each with its own life cycle for an energy
system. In addition, there are many other energy-related research areas,
including market, price, regulation, and policies. Therefore, FL has a wide
potential in energy.

##  V Conclusion and future work

FL is an emerging computing paradigm. In this paper, we presented a systematic
review of FL and its applications in the energy sector. We first introduced
the concept of FL, the technical advances especially about the research of
solutions, network structures, and global model generation algorithms. Then,
we investigated various solutions and applications of FL in energy, mainly
involving demand-response programs, user characteristics identification,
customer segmentation, energy prediction, and optimization. We also discussed
the technical challenges and opportunities of the FL technology itself and
enumerated its opportunities in the energy sector. We hope that this survey
will help researchers refine FL techniques and provide useful reference
material for researchers in the energy field.

In the future work, we will improve our review in a more systematic way and
update the latest research advances on FL and its applications in energy. We
also intend to develop an FL benchmarking and evaluation tool, as no such tool
is available to date.

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