Toward Efficient Federated Learning over Wireless Networks : Novel Frontiers in Resource Optimization

With the rise of the Internet of Things (IoT) and 5G networks, edge computing addresses critical limitations in cloud computing’s quality of service . Machine learning (ML) has become essential in processing IoT-generated data at the edge, primarily through distributed optimization algorithms that support predictive tasks. However, state-of-the-art ML models demand substantial computational and communication resources, often exceeding the capabilities of wireless devices. Moreover, training these models typically requires centralized access to datasets, but transmitting such data to the cloud introduces significant communication overhead, posing a critical challenge to resource-constrained systems. Federated Learning (FL) is a promising iterative approach that reduces communication costs through local computation on devices, where only model parameters are shared with a central server. Accordingly, every communication iteration of FL experiences costs such as computation, latency, bandwidth, and energy. Although FL enables distributed learning across multiple devices without exchanging raw data, its success is often hindered by the limitations of wireless communication overhead, including traffic congestion, and device resource constraints. To address these challenges, this thesis presents cost-effective methods for making FL training more efficient in resource-constrained wireless environments. Initially, we investigate challenges in distributed training over wireless networks, addressing background traffic and latency that impede communication iterations. We introduce the cost-aware causal FL algorithm (FedCau), which balances training performance with communication and computation costs through a novel iteration-termination method, removing the need for future information. A multi-objective optimization problem is formulated, integrating FL loss and iteration costs, with communication managed via slotted-ALOHA, CSMA/CA, and OFDMA protocols. The framework is extended to include both convex and non-convex loss functions, and results are compared with established communication-efficient methods, including heavily Aggregated Quantized Gradient (LAQ). Additionally, we develop ALAQ(Adaptive LAQ), which conserves energy while maintaining high test accuracy by dynamically adjusting bit allocation for local model updates during iterations . Next, we leverage cell-free massive multiple-input multiple-output (CFm-MIMO) networks to address the high latency in large-scale FL deployments. This architecture allows for simultaneous service to many users on the same time/frequency resources, mitigating the latency bottleneck through spatial multiplexing. Accordingly, we propose optimized uplink power allocation schemes that minimize the trade-off between energy consumption and latency, enabling more iterations under given energy and latency constraints and leading to substantial gains in FL test accuracy. In this regard, we present three approaches, beginning with a method that jointly minimizes the users’ uplink energy and FL training latency. This approach optimizes the trade-off between each user’s uplink latency and energy consumption, factoring in how individual transmit power impacts the energy and latency of other users to jointly reduce overall uplink energy consumption and FL training latency. Furthermore, to address the straggler effect, we propose an adaptive mixed-resolution quantization scheme for local gradient updates, which considers high resolution only for essential entries and utilizes dynamic power control. Finally, we introduce EFCAQ, an energy-efficient FL in CFmMIMO networks, with a proposed adaptive quantization to co-optimize the straggler effect and the overall user energy consumption while minimizing the FL loss function through an adaptive number of local iterations of users. Through extensive theoretical analysis and experimental validation, this thesis demonstrates that the proposed methods outperform state-of-the-art algorithms across various FL setups and datasets. These contributions pave the way for energy-efficient and low-latency FL systems, making them more practical for use in real-world wireless networks.