Optimizing Hierarchical Federated Learning on Non-IID Data through Coalition Formation and Gradient Projection

Core Concepts

A novel optimization method LEAP is proposed to address the impact of data distribution, execution time, and energy consumption on hierarchical federated learning, by combining coalition formation game and gradient projection.

Abstract

The paper presents a novel optimization method called LEAP (coaLition formation gamE and grAdient Projection) to address the challenges in hierarchical federated learning (HFL) on non-IID data.
The key highlights are:

LEAP transforms the data distribution optimization problem into an edge correlation problem and further optimizes the heterogeneous resource allocation problem.

LEAP constructs a coalition formation game by analyzing the relationship between edge association and edge data distribution similarity, and proves the existence of stable coalitions. It then utilizes the gradient projection method to calculate the optimal bandwidth allocation for each coalition.

LEAP determines the optimal transmission power for heterogeneous clients to ensure that the latency requirements of the tasks are met.

Experimental results on four real datasets show that LEAP can achieve a 20.62% improvement in model accuracy compared to state-of-the-art baselines. Moreover, LEAP effectively reduces transmission energy consumption by at least 2.24 times.

Stats

The training dataset size for each client is denoted as |Dn|.
The number of CPU cycles for training unit data is cn.
The CPU cycle frequency that determines the computational power is fn.
The model size is Z.
The uplink transmission rate of client n is Rn,m.
The transmission power of client n is pn,m.
The channel gain between client n and edge server m is hn,m.
The power of additive white Gaussian noise is N0.

Quotes

"LEAP is able to achieve 20.62% improvement in model accuracy compared to the state-of-the-art baselines."
"LEAP effectively reduce transmission energy consumption by at least about 2.24 times."

Key Insights Distilled From

LEAP: Optimization Hierarchical Federated Learning on Non-IID Data with Coalition Formation Game

by Jianfeng Lu,... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00579.pdf

Deeper Inquiries

How can LEAP be extended to handle dynamic client joining and leaving during the training process?

To handle dynamic client joining and leaving during the training process, LEAP can be extended by incorporating mechanisms for real-time coalition formation and adjustment. This can involve continuously monitoring the performance and data distribution of clients and dynamically updating the coalition structure based on the changing conditions.
One approach could be to implement a feedback loop where clients periodically evaluate their performance within their current coalition and assess the potential benefits of switching to another coalition. This evaluation can be based on metrics such as model accuracy, communication efficiency, and data distribution similarity. Clients can then make informed decisions about joining or leaving coalitions based on this evaluation.
Additionally, LEAP can incorporate algorithms for efficient coalition merging and splitting to accommodate dynamic changes in the client population. When new clients join or existing clients leave, the coalition formation process can be triggered to reorganize the coalitions and optimize the data distribution among the remaining clients.
By implementing these dynamic adjustment mechanisms, LEAP can adapt to changes in the client composition and ensure optimal performance in a dynamic federated learning environment.

What are the potential drawbacks or limitations of the coalition formation game approach used in LEAP, and how can they be addressed?

While the coalition formation game approach used in LEAP offers several advantages, such as optimizing data distribution and improving model performance, there are potential drawbacks and limitations that need to be considered:

Computational Complexity: The coalition formation game algorithm may have high computational complexity, especially as the number of clients and edge servers increases. This can lead to longer processing times and resource-intensive operations. To address this, optimization techniques such as parallel computing or distributed algorithms can be employed to reduce the computational burden.

Convergence Issues: The coalition formation game may face challenges in reaching a stable coalition partition, especially in dynamic environments where client preferences and data distributions change frequently. To improve convergence, adaptive learning rates and convergence criteria can be implemented to ensure that the algorithm converges to a stable solution efficiently.

Privacy Concerns: The coalition formation game involves sharing information about data distribution and client preferences, which can raise privacy concerns. To address this, privacy-preserving techniques such as differential privacy or secure multi-party computation can be integrated into the algorithm to protect sensitive information while still achieving the desired optimization goals.

Scalability: The coalition formation game approach may face scalability issues when dealing with a large number of clients and edge servers. To enhance scalability, hierarchical coalition formation strategies or clustering techniques can be employed to group clients into smaller sub-coalitions, reducing the complexity of the optimization process.

By addressing these potential drawbacks and limitations, the coalition formation game approach in LEAP can be enhanced to ensure robust performance in federated learning scenarios.

How can the proposed optimization framework be adapted to other distributed learning paradigms beyond hierarchical federated learning?

The proposed optimization framework in LEAP can be adapted to other distributed learning paradigms beyond hierarchical federated learning by modifying the coalition formation game and resource allocation strategies to suit the specific requirements of different paradigms. Here are some ways to adapt the framework:

Centralized Federated Learning: In centralized federated learning, where a central server coordinates model training, the coalition formation game can be adjusted to optimize client-server associations and data distribution. Resource allocation strategies can be tailored to minimize communication costs between clients and the central server.

Decentralized Learning: For decentralized learning scenarios where multiple nodes collaborate without a central coordinator, the optimization framework can be extended to support peer-to-peer communication and dynamic coalition formation among nodes. Resource allocation can focus on optimizing local model updates and data sharing among decentralized nodes.

Edge Computing: In edge computing environments, where computation is performed closer to the data source, the optimization framework can be customized to consider the limited resources and communication constraints at the edge devices. Coalition formation can be designed to maximize edge device collaboration and minimize latency in model training.

By adapting the coalition formation game and resource allocation strategies to the specific characteristics of different distributed learning paradigms, the optimization framework in LEAP can be effectively applied to a wide range of scenarios beyond hierarchical federated learning.