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Federated Learning and Wireless Communications
Zhijin Qin, Geoffrey Ye Li, Hao Ye
TL;DR
Federated learning training can consume substantial communication resources, especially across many wireless clients, and allocating those resources efficiently matters. This article surveys federated learning principles, communication methods, and wireless applications. Reported examples include data-exchange savings for vehicular estimation and reduced travel distance and collision probability for UAV control.
Problem
Federated learning training consumes significant communication resources, particularly with many wireless clients, making efficient resource use a central concern.
Source: §III · p. 4Method
Results
Vehicular federated estimation saves up to 79% data exchange at accuracy similar to the central solution, while federated UAV control shortens travel distance and reduces collision probability by 50%.
Source: A. Distribution Estimation for… · p. 5, B. Massive UAV Online Path Control · p. 6Takeaways & Limitations
Federated learning is presented as both dependent on communication between clients and a server and as a potential tool for improving wireless-system performance.
Source: §I · p. 1, §V · p. 6Over-the-air aggregation assumes perfect synchronization and accurate channel-state information, while real deployments may have synchronization errors and multipath delay spread.
Source: §V · p. 6
Abstract
from arXiv · showhide
Federated learning becomes increasingly attractive in the areas of wireless communications and machine learning due to its powerful functions and potential applications. In contrast to other machine learning tools that require no communication resources, federated learning exploits communications between the central server and the distributed local clients to train and optimize a machine learning model. Therefore, how to efficiently assign limited communication resources to train a federated learning model becomes critical to performance optimization. On the other hand, federated learning, as a brand new tool, can potentially enhance the intelligence of wireless networks. In this article, we provide a comprehensive overview on the relationship between federated learning and wireless communications, including basic principle of federated learning, efficient communications for training a federated learning model, and federated learning for intelligent wireless applications. We also identify some future research challenges and directions at the end of this article.
I. INTRODUCTION
Deep learning has been applied to improve communications and support wireless resource allocation, while federated learning distributes model training across local clients and a central server. This article surveys their relationship, including the communication requirements of federated learning and its potential wireless applications.
I. INTRODUCTION
- Deep learning is used for communications tasks and resource allocation, including learned signal processing and reinforcement-learning policies for wireless resources.Wireless resource-allocation problems are often NP-hard, and deep learning can reduce their complexity and improve performance in solving them.Source: §I · p. 1
II. FEDERATED LEANING
- Federated learning coordinates clients that train locally and send learned model parameters—not raw data—to a central server, reducing communication overhead and preserving privacy.It is motivated in part by privacy-sensitive tasks such as training an image-classification model on photos stored across mobile devices.Source: §I · p. 1, §II · pp. 1–2
I. INTRODUCTION
- The article surveys federated learning principles, communication-efficient training, wireless applications, and future research challenges.The authors frame federated learning as both enabled by communication between clients and server and as a potential tool for wireless-system improvement.Source: §I · p. 1
A. Basic Principles
Federated learning trains a shared model through repeated local updates and server aggregation while leaving raw data at clients. Its global objective combines client-specific losses according to local dataset sizes.
A. Basic Principles
- Federated learning keeps raw data at clients while multiple clients train under a central server and exchange model parameters.The process consists of local updates, parameter uploads, global aggregation, and server feedback to clients.Source: A. Basic Principles · p. 2
1) Loss Function:
- The global loss combines each client’s loss function in proportion to the size of that client’s dataset.Each client’s loss depends on its local data and the model parameters.Source: 1) Loss Function · p. 2
- Training aims to minimize the global loss, but the central server cannot directly calculate its gradient because the raw data is distributed across clients.The paper gives gradient descent as an example optimization approach.Source: 1) Loss Function · p. 2
2) Model Weights:
Local client updates and server-side aggregation allow gradient descent on the global objective. Whether clients send gradients or updated parameters depends on how the local update relates to the previous global parameters.
2) Model Weights:
- Each client updates its parameters from the previous global parameters using its local gradient and a step size.The server can calculate the next global parameters from local gradients when those gradients are obtained.Source: 2) Model Weights · p. 2
- When updated local parameters relate nonlinearly to the previous global parameters, clients must send updated parameters for aggregation rather than only gradients.In that case, aggregation may be more complex than the averaging rule in equation (2).Source: 2) Model Weights · p. 2
- Aggregating local gradients enables the central server to perform gradient descent on the global loss.The paper illustrates this with two clients holding equal-sized datasets.Source: 2) Model Weights · p. 2
- In the two-client example, gradient descent decreases the global loss across iterations, while each local update decreases its corresponding client loss.The server averages the two updated local parameter sets in the equal-data example.Source: 2) Model Weights · pp. 2–3
B. Practical Issues
Practical federated learning must handle iterative local optimization and imperfect, asynchronous parameter exchange. These conditions complicate simple weighted averaging and motivate methods for robust aggregation, stragglers, and transmission distortion.
1) Stochastic Gradient Descent:
- Because client losses usually lack closed forms, clients use stochastic gradient descent to estimate gradients iteratively during communication with the server.The local loss depends on raw data held at that client.Source: 1) Stochastic Gradient Descent · p. 3
B. Practical Issues
- Weighted averaging is optimal only when the server receives local parameters accurately and simultaneously, conditions that are difficult to ensure in practice.Parameter exchanges can involve limited precision, corruption, and noise; heterogeneous computation and links can also delay straggling clients.Source: 2) Robust Aggregation · p. 3
- The paper identifies robust aggregation for corrupted parameters, coded federated learning for stragglers, and broadband analog aggregation for transmission distortion and interference.The cited coded method may address stragglers and speed convergence, while the analog method results in lowered latency.Source: 2) Robust Aggregation · p. 3
2) Robust Aggregation:
The section considers client selection and communication resources in robust aggregation, warns that gradients can leak privacy information, and describes the interplay between federated learning and wireless communications.
2) Robust Aggregation:
- When client updates are negligible or their channels are poor, joint consideration of communication resources and contribution to performance can improve aggregation efficiency.Poor channels may require substantial transmission power and bandwidth to compensate for distortion.Source: 4) Privacy Leakage · p. 3
4) Privacy Leakage:
- Exchanging model parameters instead of raw data preserves privacy, but gradients can still reveal partial privacy information, so federated learning cannot guarantee complete privacy.Source: 4) Privacy Leakage · p. 3
5) Interplay of Federated Learning and Wireless Communications:
- Federated learning depends on wireless links between clients and the server, making efficient use of limited wireless resources important; wireless applications include resource allocation and localization.Source: 5) Interplay of Federated Learning and… · p. 3
C. Federated Learning vs Distributed Learning
Federated learning differs from traditional distributed learning in its data, privacy, and communication arrangements, while terminology for related learning paradigms remains unsettled. Its wireless communication demands motivate resource-efficient training techniques.
C. Federated Learning vs Distributed Learning
- Federated learning keeps data decentralized at clients, prevents the server from seeing raw data, and has clients communicate with the server to update model parameters.Clients may choose whether to participate, and their data distributions and device categories can differ.Source: C. Federated Learning vs Distributed… · p. 3
- Unlike traditional distributed learning, federated learning treats wireless communication as a bottleneck and may involve far more clients; distributed learning may permit raw-data exchange or dataset repartitioning.Source: C. Federated Learning vs Distributed… · p. 3
- The authors report no unanimous definitions for distributed, decentralized, or collaborative learning, while federated learning relies on a strict definition they identify in [5].Source: C. Federated Learning vs Distributed… · p. 3
III. COMMUNICATIONS IN FEDERATED LEARNING
- Federated training repeatedly exchanges updated model parameters, consuming substantial communication resources, particularly with many wireless clients such as in IoT applications.The section introduces gradient compression, over-the-air computation, and joint optimization as techniques suited to this communication burden.Source: §III · pp. 3–4
A. Gradient Compression
The section surveys gradient compression and over-the-air computation as communication-efficient approaches for federated learning, while noting model dependence and practical constraints on aggregation.
A. Gradient Compression
- Gradient redundancy can reach 99% in certain situations; combining quantization and sparsification can reduce the compression ratio to 2.5% while maintaining reasonable convergence.Quantization discretizes gradient elements, while sparsification zeros small-amplitude elements.Source: A. Gradient Compression · p. 4
- Deep gradient compression combines momentum correction, local gradient clipping, momentum factor masking, and warm-up training, reaching a 0.17% compression ratio in certain situations.Source: A. Gradient Compression · p. 4
- Compression performance depends strongly on the learning model, and results from image-recognition and language-processing models may not hold for wireless applications.Source: A. Gradient Compression · p. 4
B. Over-the-Air Computation
- Over-the-air computation uses simultaneous client transmissions so the wireless channel superposes local parameters and the server can obtain their weighted average directly, saving bandwidth.Figure 3 depicts parameter aggregation through over-the-air computation and spatial freedom.Source: B. Over-the-Air Computation · p. 4, Figure 3 · p. 4
- With channel distortion and client power limits, perfect aggregation may lack sufficient freedom; a sparse, low-rank optimization selects clients and adjusts transmission power and beamforming.Multiple receiver antennas offer additional freedom to assign spatial resources.Source: B. Over-the-Air Computation · p. 4
C. Joint Local Compression and Global Aggregation
The section describes combining local gradient compression with over-the-air aggregation and discusses joint optimization of communication and training decisions.
C. Joint Local Compression and Global Aggregation
- CSOverAir compresses gradients by a 1% ratio at each client, then reconstructs and aggregates gradient information from the noisy over-the-air observation.Source: C. Joint Local Compression and Global… · p. 4
- Over-the-air computation and local gradient compression both reduce communication overhead; the CSOverAir compression ratio depends heavily on compressive sensing and its recovery does not account for channel fading.Figure 4 presents the communication-budget gain for over-the-air computation with local compression and server recovery/aggregation.Source: C. Joint Local Compression and Global… · p. 5, Figure 4 · p. 5
- A control algorithm can adjust global aggregation frequency to minimize global loss given communication resources; jointly optimizing compression, resource allocation, and training remains a research direction.Source: C. Joint Local Compression and Global… · p. 5
IV. FEDERATED LEARNING FOR WIRELESS APPLICATIONS
The article surveys wireless applications of federated learning, including vehicular communications, UAV path control, and localization. In vehicular communications, federated estimation addresses resource, reliability, and latency relationships while reducing data exchange and avoiding privacy issues.
A. Distribution Estimation for Vehicular Communications
- Federated learning-based estimation saves up to 79% of data exchange while achieving accuracy similar to the centralized solution.The comparison concerns estimating critical parameters for vehicular communications.Source: A. Distribution Estimation for… · p. 5
- Federated learning-based maximum likelihood estimation estimates critical parameters linking assigned resources, link reliability, and transmission latency.These parameters are determined through extreme value theory for modeling rare events.Source: A. Distribution Estimation for… · p. 5
- Unlike classical maximum likelihood estimation, the federated approach avoids transmitting vehicle users’ data to the roadside unit, saving communication resources and avoiding privacy issues.Source: A. Distribution Estimation for… · p. 5
B. Massive UAV Online Path Control
The section describes federated learning in UAV path control and localization. The examples report reductions in UAV travel distance and collision probability, and a real-data comparison favoring Gaussian processes over a DNN for localization.
B. Massive UAV Online Path Control
- Federated-learning-based mean-field-game control reduces UAV traveling distance and collision probability by 50% each.Federated learning periodically exchanges DNN parameter sets used to solve the FPK and HJB equations.Source: B. Massive UAV Online Path Control · p. 6
- In UAV control, mean-field-game methods use coupled FPK and HJB equations to estimate population distribution and control flight direction, speed, and acceleration.Source: B. Massive UAV Online Path Control · pp. 5–6
C. Distributed Localization
- Federated localization trains models from mobile clients’ local radio-feature and location data, avoiding centralized collection that would cause privacy issues and large communication overhead.Source: C. Distributed Localization · p. 6
- On real-data testing, a Gaussian-process model with maximum-likelihood loss performed better than a DNN with least-square loss for localization.Source: C. Distributed Localization · p. 6
V. CONCLUSION AND REMARKS
The conclusion recaps the article’s treatment of federated learning principles, efficient communications, and wireless applications, while identifying open challenges. These include deployment imperfections in over-the-air aggregation, local-global model coordination, residual privacy leakage, and further wireless applications.
V. CONCLUSION AND REMARKS
- The article presents federated learning principles and efficient communications, discusses wireless application examples, and identifies remaining challenges.Source: §V · p. 6
- Over-the-air aggregation proposals assume perfect synchronization and accurate channel-state information, while real deployment imperfections such as multipath delay spread remain to be addressed.Source: §V · p. 6
- Local client models can be tailored to local purposes but differ from the server’s global model, raising questions about updates and gradient processing and transmission.A meta-learner is proposed as one possible way to learn gradients for local and global models.Source: §V · p. 6
- Federated learning improves privacy relative to sending raw data to the server, but partial privacy leakage remains.Source: §V · p. 6
- Beyond distributed localization, mobile edge caching and resource allocation in vehicular networks are identified as expected wireless applications.Source: §V · p. 6