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Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying


Core Concepts
Exploiting mobility for faster convergence in asynchronous federated learning.
Abstract
This paper explores the impact of mobility on the convergence performance of asynchronous Federated Learning (FL) by utilizing opportunistic mobile relaying. Clients can indirectly communicate with the server through other clients serving as relays, enabling quicker model updates and fresher global models. The proposed FedMobile algorithm addresses key questions on when and how to relay, achieving a convergence rate of O(1/√NT). By considering data manipulation before relaying, costs are reduced and privacy is enhanced. Experimental results confirm the theoretical findings on synthetic and real-world datasets.
Stats
FedMobile achieves a convergence rate O(1/√NT). The proposed method outperforms state-of-the-art asynchronous FL methods by reducing time consumption significantly.
Quotes

Deeper Inquiries

How does the client-client meeting rate impact the efficiency of FedMobile

The client-client meeting rate plays a crucial role in determining the efficiency of FedMobile. A higher client-client meeting rate, represented by ρ, increases the likelihood of finding qualified relays for both uploading and downloading data. This increased interaction frequency allows clients to exchange information more frequently, leading to faster convergence rates in Federated Learning systems. With more opportunities for relay communications, clients can upload their local updates sooner or download fresher global models from other clients acting as relays. Therefore, a higher client-client meeting rate enhances the effectiveness of opportunistic relaying in improving communication efficiency and accelerating convergence in FedMobile.

What are the implications of using multiple upload/download relays in FedMobile

Introducing multiple upload/download relays in FedMobile can have significant implications on the system's performance. By allowing each client to utilize several relays between consecutive server meetings, the algorithm can leverage diverse communication paths to enhance data transmission efficiency and speed up model convergence. Multiple relays provide redundancy and flexibility in data exchange processes, reducing bottlenecks caused by limited relay availability or potential relay failures. Moreover, employing multiple upload/download relays enables parallel processing of data transmissions across different routes simultaneously. This parallelism not only speeds up communication but also improves fault tolerance within the system by mitigating risks associated with single-point failures or delays. However, managing multiple relays requires careful coordination and optimization strategies to ensure that redundant information is not transmitted unnecessarily while maximizing the benefits of opportunistic mobile relaying. Proper relay selection algorithms and dynamic routing mechanisms are essential for efficient utilization of multiple upload/download paths while maintaining data integrity and minimizing latency.

How can the concept of opportunistic relaying be applied to other machine learning algorithms or systems

The concept of opportunistic relaying demonstrated in FedMobile can be applied beyond Federated Learning algorithms to various machine learning systems or distributed computing environments where intermittent connectivity or sporadic interactions exist among network nodes. In wireless sensor networks (WSNs), opportunistic relaying techniques can improve data collection efficiency by leveraging intermediate nodes as temporary storage points before transmitting information to base stations. By opportunistically selecting reliable relay nodes based on signal strength or proximity metrics, WSNs can optimize energy consumption and extend network coverage without compromising data reliability. Furthermore, opportunistic relaying principles can be integrated into edge computing frameworks to facilitate resource sharing among edge devices for collaborative inference tasks or federated analytics scenarios. By dynamically forming ad-hoc relay chains based on real-time network conditions and device capabilities, edge computing systems can enhance computational offloading strategies while ensuring low-latency processing for time-sensitive applications. Overall, applying opportunistic relaying concepts outside Federated Learning opens up opportunities for enhancing communication resilience, optimizing resource utilization, and improving overall system performance across diverse machine learning paradigms and distributed computing architectures.
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