l participated devices, the optimization problem is formulated as (9.8a) (9.8b) , vt, (9.8c) <P<ma(mt)EMT, (9.8d) (9.8e) where W'm is the data rate weight of device m scheduled at round t. In FL, PS performs weighted average to generate the current global model; hence, a natural selection for the data rate weight can be W'm = 1Pml, which also clearly outlines the significance of each device's update. Am {0,1} is a binary variable that equals 1 if device m is scheduled at t and is 0 otherwise. Here, the constraint in (9.8b) corresponds to constraint C1, constraint in (9.8c) corresponds to constraint C2, and constraint in (9.8d) corresponds to constraint C3. Finding the maximum weight sum data rate under these constraints involves travers- ing all possible scheduling patterns, which possess very high complexity when the number of total devices is large and selected devices for scheduling are small, i.e. M > K. Toward that, we propose the following scheduling algorithm to address this complexity issue and power allocation to solve the optimization problem (9.8a). 9 Wireless Systems for Distributed Machine Learning 9.5 Scheduling Algorithm and Power Allocation Figure 9.2 shows the diagram of the user scheduling. Each column represents a FL round for model update, and there are a total of T columns. Each block in a specific column repre- sents a scheduled user and at most K users are scheduled to participate FL update in each round. The power of the scheduled user k in round t is pk.(i1,i2, ig), (j1,j2, and (11,l2, lk) are different user combinations. For the proposed joint scheduling and power allocation scheme, first, all possible user schedules are found. Then optimal power allocation is applied for each schedule to find the optimal one. The scheduling problem which aims to maximize weighted sum rate is trans- formed under graph theory. Specifically, we introduce the maximum weight independent set problem first. An independent set is a sub-graph of an undirected graph where there exists no edge between any two vert