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Wireless Communications for Collaborative Federated Learning

Mingzhe Chen, H. Vincent Poor, Walid Saad, Shuguang Cui

arXiv:2006.02499v2cs.ITcs.LG

TL;DR

Resource and privacy constraints limit direct data sharing by IoT devices, while original federated learning still depends on direct connections to a central controller. The paper introduces collaborative federated learning and communication techniques for less centralized, more effective wireless deployment.

  • Problem

    IoT devices may be unable or unwilling to transmit collected data because of privacy and resource constraints, while original FL requires direct device connections to a central controller.

  • Method

    The paper proposes collaborative federated learning and develops wireless communication techniques for network formation, device scheduling, mobility management, and coding.

  • Results

    The paper presents CFL as a framework for implementing federated learning with less reliance on a central controller and provides communication guidance for deploying it over wireless systems.

  • Takeaways & Limitations

    CFL extends federated learning toward edge IoT settings where centralized connectivity is constrained, while the proposed wireless techniques target its performance.

Abstract

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Internet of Things (IoT) services will use machine learning tools to efficiently analyze various types of data collected by IoT devices for inference, autonomy, and control purposes. However, due to resource constraints and privacy challenges, edge IoT devices may not be able to transmit their collected data to a central controller for training machine learning models. To overcome this challenge, federated learning (FL) has been proposed as a means for enabling edge devices to train a shared machine learning model without data exchanges thus reducing communication overhead and preserving data privacy. However, Google's seminal FL algorithm requires all devices to be directly connected with a central controller, which significantly limits its application scenarios. In this context, this paper introduces a novel FL framework, called collaborative FL (CFL), which enables edge devices to implement FL with less reliance on a central controller. The fundamentals of this framework are developed and then, a number of communication techniques are proposed so as to improve the performance of CFL. To this end, an overview of centralized learning, Google's seminal FL, and CFL is first presented. For each type of learning, the basic architecture as well as its advantages, drawbacks, and usage conditions are introduced. Then, three CFL performance metrics are presented and a suite of communication techniques ranging from network formation, device scheduling, mobility management, and coding is introduced to optimize the performance of CFL. For each technique, future research opportunities are also discussed. In a nutshell, this article will showcase how the proposed CFL framework can be effectively implemented at the edge of large-scale wireless systems such as the Internet of Things.

I. INTRODUCTION

Wireless constraints, privacy concerns, and centralized connectivity requirements limit conventional learning over IoT networks. The paper introduces collaborative federated learning (CFL) and communication techniques to address these limitations.

  • Motivation: Edge IoT devices may be unable or unwilling to share collected data because of privacy issues and resource constraints.Relevant constraints include bandwidth and transmit power limitations.
  • Motivation: Federated learning enables devices to train a shared model while exchanging model parameters rather than collected data.This approach preserves data privacy and may reduce network traffic congestion.
  • Research gap: Wireless links introduce limited participation, transmission errors, and delays that can affect federated-learning performance.Limited wireless resources mean only a subset of devices may use FL, while transmitted models remain vulnerable to channel impairments.
  • Research gap: Prior surveys focused on using federated learning for communications and did not analyze less centralized implementations or communication-based optimization.They primarily considered Google's original FL, which requires devices to transmit models to a central controller.
  • Contributions: The paper introduces collaborative FL, which combines collaborative and federated learning so edge devices can participate without connecting to a central controller.The article also overviews centralized learning, original FL, and CFL, including their advantages, drawbacks, and usage conditions.
  • Contributions: The paper presents three CFL performance metrics and communication techniques covering network formation, device scheduling, mobility management, and coding.Each technique is discussed with motivation, an illustrative example, and future research opportunities.

II. PRELIMINARIES AND OVERVIEW

Centralized learning places one model at a base station or cloud and requires devices to transmit their data, while wireless federated architectures exchange model information instead. These designs differ in privacy, resource requirements, and exposure to wireless transmission impairments.

  • Centralized Learning: Centralized learning uses one ML model at a base station or cloud, with devices sending their data to the central controller for training.The trained model is then transmitted back to the devices.
  • Centralized Learning: Centralized learning can directly find a globally optimal ML model that minimizes the learning-loss function value.Its training process is completed centrally.
  • Overview: The paper compares centralized learning, original federated learning, and collaborative federated learning through their architectures, advantages, drawbacks, and usage conditions.These comparisons are summarized in the paper's overview materials.
  • Centralized Learning: Centralized learning exposes collected device data to the base station and requires substantial network- and device-level overhead and resources.These drawbacks accompany the centralized collection and training process.

2) Original Federated Learning:

Original federated learning (OFL) lets devices train local models and a base station aggregate and redistribute them, preserving privacy but requiring direct device-to-BS communication. Collaborative federated learning (CFL) relaxes this connectivity requirement by using neighboring devices, enabling participation when BS links are unavailable or delayed.

  • OFL operation: OFL trains local models at devices, aggregates them at the BS, and redistributes the resulting global model iteratively.At convergence, the global and local FL models become equal.
  • OFL advantages and limitations: OFL preserves data privacy and uses less overhead than centralized ML, but all devices must transmit local model parameters to the BS.Imperfect or dynamic wireless transmission can significantly affect OFL convergence time and performance.
  • CFL framework: CFL enables devices to perform FL without transmitting local FL models to the BS by associating devices that cannot reach the BS with neighboring users.Each device aggregates received local models and trains its own local model.
  • Simulation comparison: CFL can reduce device energy consumption when a device transmits model parameters to a neighboring device instead of the BS.The example identifies device b transmitting to device a rather than directly to the BS.
  • Usage conditions: The choice between centralized learning, OFL, and CFL depends on data-sharing willingness, model and dataset sizes, BS participation, and device-to-BS delay.OFL is a special case of CFL when every device connects to every other device.

III. PERFORMANCE OF CFL OVER WIRELESS NETWORKS

CFL performance over wireless networks is assessed using loss function value, convergence time, and reliability. Wireless errors, delays, limited resources, and restricted participation can worsen training outcomes, motivating wireless-aware solutions.

  • Performance metrics: CFL performance is evaluated using loss function value, convergence time, and reliability.The loss function is an objective that devices minimize by adjusting ML model parameters.
  • Loss function value: Wireless transmission errors and delays can negatively affect the loss function during CFL training.The loss function depends on the local FL models of participating devices.
  • Loss function value: Limited energy and computing resources restrict the participating device subset, reducing training data samples and increasing the loss function value.Limited spectrum resources also restrict the number of devices engaging in FL.
  • Wireless factors: Tables summarize wireless factors affecting the FL loss function and suggested solutions.The supplied passages identify this summary as Table III.

2) Convergence Time:

CFL convergence time combines communication, computation, and iteration costs. These costs depend on model size, wireless data rate, device resources, local update choices, spectrum, energy, distance, and target training requirements.

  • Convergence-time components: CFL convergence time consists of FL model transmission delay, local-model training time, and the number of global model updates required for convergence.These components capture communication, computation, and iterative learning costs.
  • Communication and computation: Transmission delay depends on FL model parameter data size and wireless-link data rate, while local training time depends on model size, device resources, and local update count.Increasing local model updates decreases the number of global model updates.
  • Wireless constraints: Limited spectrum resources restrict the number of devices that can engage in FL and therefore affect model-update timing.The passage identifies spectrum as a wireless factor affecting convergence time.
  • Energy consumption: CFL training energy includes local-model transmission and update, global-model transmission, and global-model aggregation.Devices spend energy on local transmission and updates, while the BS spends energy on global transmission and aggregation.
  • Energy consumption: A tradeoff exists between local-model update energy and transmission energy, with total CFL energy depending on model size, BS-device distance, convergence-time requirements, and target loss value.Suggested solutions for these energy factors are summarized in Table II.

4) Reliability:

CFL reliability is the probability of achieving a target FL loss value, and it depends on wireless channel conditions. Higher transmit power reduces erroneous local models and increases reliability, while transmission errors can prevent the target from being reached.

  • Reliability definition: CFL reliability is defined as the probability that a CFL algorithm achieves a target FL loss function value.This metric evaluates whether wireless CFL training reaches the specified loss target.
  • Transmission errors: Imperfect wireless transmissions can cause local FL models to be abandoned, reducing the number of models used to generate the global model.This reduction affects CFL convergence and loss-function behavior.
  • Reliability limitation: A CFL algorithm may fail to achieve a target FL loss function value because of imperfect wireless transmissions.Thus, wireless conditions impose a direct reliability boundary.
  • Wireless dependence: Higher transmit power decreases erroneous local FL models and increases CFL reliability.The reliability of CFL therefore depends on wireless channel conditions.

IV. COMMUNICATION TECHNIQUES FOR COLLABORATIVE FEDERATED LEARNING

The paper overviews communication techniques for improving collaborative federated learning (CFL) performance.

  • The overview introduces communication techniques intended to improve CFL performance.

A. Network Formation

Network formation determines CFL performance and raises design challenges involving topology optimization, dynamic training, and incomplete network information.

  • CFL network topology directly affects training complexity and convergence time.
  • More device links reduce convergence iterations by increasing the frequency of local FL model sharing.
  • Optimal CFL network formation: Optimal topology depends on the CFL metrics being optimized and requires a utility function combining dependent metrics with topology.
  • Optimal CFL network formation: Centralized topology search may find a global optimum but requires all devices’ locations or wireless channel conditions, which is impractical in large-scale dynamic IoT systems.
  • Optimal CFL network formation: Distributed network formation can use game theory because each device chooses neighboring links while CFL performance depends on the entire graph.
  • Optimal CFL network formation: CFL network formation can be dynamic and require far-sighted decision making, an angle studied in limited prior work.
  • Network formation with asynchronous training: Asynchronous training requires topology adaptation because participating devices and local-model transmission requirements vary over time.
  • Network formation with asynchronous training: Topology updates can improve metrics such as convergence time but introduce overhead through wireless resource reallocation and network-state sharing.

B. Device Scheduling

Device scheduling in CFL must account for energy and wireless constraints, data importance, multiple tasks, and device mobility.

  • Energy and wireless resource constraints limit how many devices can engage in CFL at each iteration.
  • Data importance-aware device scheduling: Data importance-aware scheduling should jointly consider sample count, data distribution, and data uniqueness to improve convergence speed.
  • Data importance-aware device scheduling: Because devices cannot share data, data importance may need to be inferred from transmitted local FL model parameters.
  • Data importance-aware device scheduling: Scheduling must balance faster convergence from updating high-importance devices against the increased loss function value.
  • Device scheduling for multiple FL tasks: For multiple FL tasks, scheduling must prioritize model training and transmission according to each task’s convergence-time and loss-value requirements.
  • Device scheduling and network formation for mobile devices: Mobility changes device connections and wireless performance, potentially increasing connection changes and slowing CFL training.
  • Device scheduling and network formation for mobile devices: Mobile-device scheduling and topology policies should model spatio-temporal correlations and optimize policy updates across time slots.
  • Device scheduling and network formation for mobile devices: For devices moving in parallel, constant separation means location changes do not increase local FL model transmission delay.

C. Coding

Coding techniques reduce CFL communication costs and address wireless impairments, while introducing tradeoffs between compression and learning accuracy.

  • Source, channel, and gradient coding are proposed to improve CFL performance.
  • Source coding: With R = 4 bits per local-model element, quantization-based CFL almost matches uncoded CFL performance while reducing transmission delay.
  • Source coding: R = 4 quantization-based CFL performs better than the R = 2 version because fewer bits increase coding distortion and reduce identification accuracy.
  • Heterogeneous source coding design: Heterogeneous source coding assigns different bit budgets or coding methods across devices to reduce coding energy consumption and loss.
  • Heterogeneous source coding design: CFL coding must determine how many local models each device encodes and the bits used for models sent to multiple recipients.
  • Gradient coding for avoiding stragglers: Stragglers with high transmission or computation delays can reduce the number of devices able to perform CFL.
  • Gradient coding for avoiding stragglers: Traditional gradient coding cannot be directly used because it requires dataset sharing, which CFL disallows; a novel data-private scheme is needed.

V. CONCLUSION

The article introduces collaborative FL (CFL) and communication techniques for optimizing and deploying it across edge IoT devices. It identifies open research problems and points to future applications enabled by the framework.

  • The article proposes collaborative FL (CFL) and wireless communication techniques to optimize its performance.
  • These techniques provide guidance for reliably deploying CFL across edge IoT devices.
  • The discussion identifies open problems that must be considered when designing and deploying CFL for IoT systems.
  • CFL is presented as an architecture that could support applications including mobile keyboard prediction, IoT device identification and monitoring, and extreme event detection for autonomous vehicles.
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