Federated learning aims to construct a global model that fits the dataset distributed across local devices without direct access to private data, leveraging communication between a server and the local devices. In the context of a practical communication scheme, we study the completion time required to achieve a target performance. Specifically, we analyze the number of iterations required for federated learning to reach a specific optimality gap from a minimum global loss. Subsequently, we characterize the time required for each iteration under two fundamental multiple access schemes: 1) time-division multiple access (TDMA) and 2) random access (RA). We propose a step-wise batch allocation, demonstrated to be optimal for TDMA-based federated learning systems. Additionally, we show that the nonzero batch gap between devices provided by the proposed step-wise batch allocation significantly reduces the completion time for RA-based learning systems. Numerical evaluations validate these analytical results through real-data experiments, highlighting the remarkable potential for substantial completion time reduction.

We investigate Age of Information (AoI) in an Internet of Things (IoT) sensor network where a single relay terminal connects multiple IoT sensors to their corresponding destination nodes. In order to minimize average weighted sum AoI, joint optimization of sampling and updating policy of a relay is studied. For error-free and symmetric case where weights are identical, the necessary and sufficient condition for optimal policy is figured out. We also obtain the minimum average sum AoI in a closed-form expression which can be interpreted as the fundamental limit of sum AoI in a single relay network. Moreover, we prove that the greedy policy is optimal for minimizing the average sum AoI at the destination nodes in the error-prone symmetric network. For general case where weights are arbitrarily given, we propose a scheduling policy obtained via deep reinforcement learning.

We consider distributed machine learning at the wireless edge, where a parameter server builds a global model with the help of multiple wireless edge devices that perform computations on local dataset partitions. Edge devices transmit the result of their computations (updates of current global model) to the server using a fixed rate and orthogonal multiple access over an error prone wireless channel. In case of a transmission error, the undelivered packet is retransmitted until successfully decoded at the receiver. Leveraging on the fundamental tradeoff between computation and communication in distributed systems, our aim is to derive how many edge devices are needed to minimize the average completion time while guaranteeing convergence. We provide upper and lower bounds for the average completion and we find a necessary condition for adding edge devices in two asymptotic regimes, namely the large dataset and the high accuracy regime. Conducted experiments on real datasets and numerical results confirm our analysis and substantiate our claim that the number of edge devices should be carefully selected for timely distributed edge learning.

User mobility has a large effect on optimal content placement in device-to-device (D2D) caching networks. Since a typical user can communicate neighboring users who stay in the D2D communication area of the typical user, the optimal content placement should be changed according to the user mobility. Under consideration of randomness of incoming and outgoing users, we formulate an optimization problem to minimize the average data load of a BS. It is proved that minimization of the average data load of a BS can be transformed to maximization of a monotonic submodular function with a matroid constraint, for which a greedy algorithm can find near-optimal solutions. Moreover, when motions of neighboring users are rapid, the optimal content placement is derived in closed-form, aided by reasonable approximation and relaxation. In the high mobility regime, the optimal content placement is shown to cache partial amounts of the most popular contents.

This paper investigates optimal content placement for wireless femto-caching network. The average bit error rate (BER) is formulated as a function of content placement under wireless fading. To minimize the average BER, we propose a greedy algorithm finding optimal content placement with low-computational complexity. Exploiting the property of the optimal content placement which we derive, the proposed algorithm can be performed over considerably reduced search space. Contrary to the optimal content placement without consideration of wireless fading aspects, we reveal that optimal content placement can be reached by balancing a tradeoff between two different gains: file diversity gain and channel diversity gain. Moreover, we also identify the conditions that the optimal placement can be found without running the proposed greedy algorithm and derive the corresponding optimal content placement in closed form.

This paper considers a cell with many caching helpers, such as femto-base stations with memories for caching files. We formulate average bit error rate (BER) as a function of caching placement in a Rayleigh flat fading channel. In order to minimize the average BER, we propose an algorithm which finds a caching placement in greedy way and prove that the caching placement found by the proposed greedy algorithm is optimal. We also show that the proposed greedy algorithm has complexity independent of file library size. It is shown that the optimal caching placement is not just caching all different files; however, for a given environment, the optimal caching placement is to balance between channel diversity gain achieved by caching the same file in multiple helpers and caching diversity gain achieved by caching different files in helpers to enlarge a set of cached-file.